System for providing graphical illustration of possible outcomes and side effects of the use of treatment parameters with respect to at least one body portion based on datasets associated with predictive bases

ABSTRACT

An apparatus, device, methods, computer program product, and system are described that provide a graphical illustration of a first possible outcome of a use of a treatment parameter with respect to at least one body portion, based on a first dataset associated with a first predictive basis, and that modify the graphical illustration to illustrate a second possible outcome of the use of the treatment parameter, based on a second dataset associated with a second predictive basis.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to, claims the earliest availableeffective filing date(s) from (e.g., claims earliest available prioritydates for other than provisional patent applications; claims benefitsunder 35 USC §119(e) for provisional patent applications), andincorporates by reference in its entirety all subject matter of thefollowing listed application(s) (the “Related Applications”) to theextent such subject matter is, not inconsistent herewith; the presentapplication also claims the earliest available effective filing date(s)from, and also incorporates by reference in its entirety all subjectmatter of any and all parent, grandparent, great-grandparent, etc.applications of the Related Application(s) to the extent such subjectmatter is not inconsistent herewith. The United States Patent Office(USPTO) has published a notice to the effect that the USPTO's computerprograms require that patent applicants reference both a serial numberand indicate whether an application is a continuation or continuation inpart. Kunio, Benefit of Prior-Filed Application, USPTO ElectronicOfficial Gazette, Mar. 18, 2003 athttp://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm. Thepresent applicant entity has provided below a specific reference to theapplication(s) from which priority is being claimed as recited bystatute. Applicant entity understands that the statute is unambiguous inits specific reference language and does not require either a serialnumber or any characterization such as “continuation” or“continuation-in-part.” Notwithstanding the foregoing, applicant entityunderstands that the USPTO's computer programs have certain data entryrequirements, and hence applicant entity is designating the presentapplication as a continuation in part of its parent applications, butexpressly points out that such designations are not to be construed inany way as any type of commentary and/or admission as to whether or notthe present application contains any new matter in addition to thematter of its parent application(s).

RELATED APPLICATIONS

-   1. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation in part of currently    co-pending United States patent application entitled Data Techniques    Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord,    and Lowell L. Wood, Jr., as inventors, U.S. application Ser. No.    11/222,031, filed Sep. 8, 2005.-   2. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation in part of currently    co-pending United States patent application entitled Data Techniques    Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord,    and Lowell L. Wood, Jr., as inventors, U.S. application Ser. No.    11/241,868, filed Sep. 30, 2005.-   3. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation in part of currently    co-pending United States patent application entitled Accessing Data    Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord,    and Lowell L. Wood, Jr., as inventors, U.S. application Ser. No.    11/262,499, filed Oct. 28, 2005.-   4. For purposes of the USPTO extra-statutory requirements, the    present application constitutes a continuation in part of currently    co-pending United States patent application entitled Accessing Data    Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord,    and Lowell L. Wood, Jr., as inventors, U.S. application Ser. No.    11/286,133, filed Nov. 23, 2005.

TECHNICAL FIELD

This description relates to data handling techniques.

SUMMARY

An embodiment provides a method. In one implementation, the methodincludes but is not limited to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis, and modifying the graphical illustration toillustrate a second possible outcome of the use of the treatmentparameter, based on a second dataset associated with a second predictivebasis. In addition to the foregoing, other method aspects are describedin the claims, drawings, and text forming a part of the presentdisclosure.

An embodiment provides a computer program product. In oneimplementation, the computer program product includes but is not limitedto a signal-bearing medium bearing at least one of one or moreinstructions for providing a graphical illustration of a first possibleoutcome of a use of a treatment parameter with respect to at least onebody portion, based on a first dataset associated with a firstpredictive basis, and the signal bearing medium bearing one or moreinstructions for modifying the graphical illustration to illustrate asecond possible outcome of the use of the treatment parameter, based ona second dataset associated with a second predictive basis. In additionto the foregoing, other computer program product aspects are describedin the claims, drawings, and text forming a part of the presentdisclosure.

An embodiment provides a system. In one implementation, the systemincludes but is not limited to a computing device and instructions. Theinstructions when executed on the computing device cause the computingdevice to provide a graphical illustration of a first possible outcomeof a use of a treatment parameter with respect to at least one bodyportion, based on a first dataset associated with a first predictivebasis, and modify the graphical illustration to illustrate a secondpossible outcome of the use of the treatment parameter, based on asecond dataset associated with a second predictive basis. In addition tothe foregoing, other system aspects are described in the claims,drawings, and text forming a part of the present disclosure.

An embodiment provides a graphical user interface. In oneimplementation, the user interface includes but is not limited to atleast a first portion configured to receive a first request to provide agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis, at least asecond portion configured to receive a second request to provide amodified graphical illustration of a second possible outcome of the useof the treatment parameter, based on a second dataset associated with asecond predictive basis, and at least a third portion configured toillustrate the graphical illustration and the modified graphicalillustration. In addition to the foregoing, other graphical userinterface aspects are described in the claims, drawings, and textforming a part of the present disclosure.

In addition to the foregoing, various other embodiments are set forthand described in the text (e.g., claims and/or detailed description)and/or drawings of the present description.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processesdescribed herein, as defined by the claims, will become apparent in thedetailed description set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example clinical system in which embodiments maybe implemented, perhaps in a device.

FIG. 2 illustrates certain alternative embodiments of the clinicalsystem of FIG. 1.

FIG. 3 illustrates an alternative embodiment of treatment dataassociated with the clinical system of FIG. 1.

FIG. 4 illustrates another alternative embodiment of treatment dataassociated with the clinical system of FIG. 1.

FIG. 5 illustrates another alternative embodiment of treatment dataassociated with the clinical system of FIG. 1, with specific examples oftreatment data.

FIG. 6 illustrates additional alternative embodiments of treatment dataassociated with the clinical system of FIG. 1, with specific examples oftreatment data.

FIG. 7 illustrates additional alternative embodiments of treatment dataassociated with the clinical system of FIG. 1, with specific examples oftreatment data.

FIG. 8 illustrates an example screenshot of a graphical user interfacefor accessing predictive data.

FIG. 9 illustrates an alternative embodiment of the clinical system ofFIG. 1 in which the clinical system is configured to provide access topredictive data.

FIG. 10 illustrates an operational flow representing example operationsrelated to accessing predictive data.

FIG. 11 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 12 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 13 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 14 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 15 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 16 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 17 illustrates an alternative embodiment of the example operationalflow of FIG. 10.

FIG. 18 illustrates a partial view of an example computer programproduct that includes a computer program for executing a computerprocess on a computing device.

FIG. 19 illustrates an example system in which embodiments may beimplemented.

The use of the same symbols in different drawings typically indicatessimilar or identical items.

DETAILED DESCRIPTION

FIG. 1 illustrates an example clinical system 100 in which embodimentsmay be implemented. The clinical system 100 includes a treatment system102. The treatment system 102 may be used, for example, to store,recall, access, process, implement, or otherwise use information that isbeneficial in a clinical setting(s). For example, the treatment system102 may be used to diagnose or treat patients by storing and/orproviding information as to whether or how treatment agents) may beapplied to a specific region(s) of interest of the human body, such as,for example, a lobe of the lungs, breast tissue, cancerous tissue at acertain bodily location, or other such regions of interest. As a furtherexample, the treatment system 102 may provide information as to whetherand/or how to minimize or avoid application of such treatment agents toregions of non-interest (for example, regions to which the treatmentagent(s) should not be applied, in order to avoid, e.g., problematicside effects and other undesired results). On the basis of such clinicalinformation, for example, targeted applications of treatment agents(e.g., medication, imaging agents, or other beneficial medical agents)may be carried out in a manner that achieves a desired outcome, whileminimizing or eliminating unwanted applications to non-targeted bodilyregions.

In FIG. 1, the treatment system 102 is used by a clinician 104. Theclinician 104 may, for example, use the treatment system 102 to enter,store, request, or access clinical information such as, for example, thevarious examples provided herein. The clinician 104 may generallyrepresent, for example, any person involved in health care, including,for example, a doctor, a nurse, a physician's assistant, or a medicalresearcher. The clinician 104 also may represent someone who is involvedin health care in the sense of developing, managing, or implementing thetreatment system 102, e.g., a software developer with clinical knowledge(or access to clinical knowledge), a database manager, or an informationtechnologies specialist. Even more generally, some or all of variousfunctions or aspects described herein with respect to the clinician 104may be performed automatically, e.g., by an appropriately-designed andimplemented computing device, or by software agents or other automatedtechniques.

A patient 106 generally represents any person with an illness, injury,or disease, or who is thought to potentially have such an illness,injury, or disease, or who may be wholly or partially healthy but who isnonetheless studied in order to determine information about such anillness, injury, or disease. The patient 106 also may represent orinclude other diagnostic and/or animal subjects that may be used inorder, for example, to determine an efficacy of a particular medicationor treatment, specific examples of which are provided herein. Thepatient 106 may represent a particular patient in a given clinicalsetting, such as in a doctor's office, or in a hospital, who is to bediagnosed and/or treated using the treatment system 102. The patient 106also may represent the more abstract notion of a class of patients(e.g., patients having a certain age, gender, race, genetic makeup, ordisposition to illness or disease), or, even more generally, mayrepresent the general notion of a generic patient during basic researchand/or development or application of various medical treatments orprocedures. In this latter sense, the patient 106 may also represent anon-human animal (such as a primate) believed to be sufficiently similarto a human for the particular purposes that they may usefully substitutefor such for the particular purposes.

As such, the patient 106 generally possesses or is associated with, forexample, some or all of the various organs, systems, organ systems,organ subsystems, diseased tissue, and/or healthy tissue that may befound in the body. In FIG. 1, the patient 106 is illustrated as having alung 108 and a pancreas 110, so that these (and other) body parts may beused as the bases for the specific examples given herein. Of course,many other applications of the treatment system 102 exist, over andabove the examples provided herein.

In an exploded portion 108 a of the lung 108, various example elementsare illustrated, although not drawn to scale for the purposes of clarityand ease of illustration and description. For example, the lung 108 mayinclude a healthy tissue portion 112, and a diseased tissue portion 114.The healthy tissue 112 may include, for example, healthy lung tissue,while the diseased tissue 114 may include, for example, a tumor or othercancerous tissue.

The lung 108 also may include a blood vessel 116, which is illustratedin a cut-away view, and which includes a tissue component 118 known as,by way of example nomenclature, the endothelium, endothelial layer, orendothelial cells. The endothelium or endothelial layer 118 generallyrefers to a layer of cells that lines an interior of a portion of thecirculatory system, such as the blood vessel 116. In FIG. 1, the bloodvessel 116 and the endothelial layer 118 are illustrated as being in thevicinity of the diseased tissue 114. In contrast, an example of a bloodvessel 120 is illustrated that contains endothelial layer 122. The bloodvessel 120 is shown as being in the vicinity of the healthy tissue 112of the lung 108.

Certain properties of the endothelial layer 118 and the endotheliallayer 122 may enable the targeted delivery of one or more treatmentagents to a vicinity of the diseased tissue 114 and the healthy tissue112, respectively. For example, blood (and other cells containedtherein) will be transported within and along a length of the bloodvessel 116, where the length of the blood vessel 116 naturally extends arelatively long distance in either direction toward/away from thediseased tissue 114. However, cells of the endothelial layer 118 thathave developed and/or grown over a period of time in a vicinity of thediseased tissue 114 may exhibit characteristics that are unique, oressentially unique, to a site on the endothelial layer 118 in thatparticular vicinity.

For example, the diseased tissue 114 may include a tumor that has grownover a period of time. During that period of time, a correspondinggrowth or development of a site on the endothelial layer 118 mayreflect, or otherwise be correlated with and/or affected by, the growthof the diseased tissue (tumor) 114. This correlation between the historyor ancestry of the site on the endothelial layer 118 in the vicinity ofthe diseased tissue 114 may result in unique, or almost unique,properties of the tissue ancestry-correlated site, such as, for example,a display of specific and identifiable proteins. Moreover, similarcomments may apply to a tissue ancestry-correlated site along theendothelial layer 122 of the blood vessel 120, in the vicinity of thehealthy tissue 112. In this way, each such tissue ancestry-correlatedsite, whether in the lung or in other sites in the body, may be used toprovide, effectively, a molecular-level address that specifies alocation within the body, e.g., a location of the diseased tissue 114and/or the healthy tissue 112.

Accordingly, such tissue ancestry-correlated sites may be used to directtreatment agents (such as, for example, medications, imaging agents, orradio-immunotherapy agents) in a desired fashion. For example, asdescribed in more detail in certain examples provided herein,radionuclides may be applied to the diseased tissue 114.

In this regard, it should be understood that, without use of the tissueancestry-correlated site(s) described herein, it may be difficult todirect such treatment agents to desired body regions with a necessary ordesired level of precision. For example, many treatment agents may bedelivered by injection (or by other delivery modalities, e.g.,swallowing or absorption through the skin) into a bloodstream of thepatient 106. However, without an effective way to direct the treatmentagents once in the bloodstream, a positive impact of the treatmentagents may be reduced or eliminated. Moreover, ancillary delivery of thetreatment agents to undesired regions (e.g., delivery of radionuclidesto the healthy tissue 112 and/or to the pancreas 110 or other organs)may result in harm to the patient 106. Such harm may be particularlyacute or problematic in cases where, for example, a concentration,dosage, or amount of the treatment agent in the bloodstream is requiredto be increased relative to an optimal treatment amount, simply toensure that some portion of the treatment agent reaches and affects adesired end target. Similar comments may apply to other treatmentmodalities. For example, treatment of the diseased tissue 114 (e.g., atumor) may be performed by radiation therapy in which the patient isexposed to radiation, and, again, the net effect of such treatment(s)may be negative due to harm caused by the radiation to the healthytissue 112.

As just described, then, tissue ancestry-correlated sites may existwithin and along the endothelial layers 118 and/or 122, in the vicinityof correlated tissues that may serve as target(s) (e.g., the diseasedtissue 114) for certain treatment agent(s). For example, thesetarget-related tissue ancestry-correlated sites may include, asdescribed herein, certain proteins that may be known to bind to/withcertain other agents. In one specific example discussed herein, atarget-related tissue ancestry-correlated binding site includes aprotein, aminopeptidase-P (APP), that is known to bind with an agentsuch as, for example, I-labeled monoclonal antibodies. If a treatmentagent (such as, for example, radionuclides) is associated with thetarget-related tissue ancestry-correlated binding agent (e.g., theI-labeled monoclonal antibodies), then injection of the target-relatedtissue ancestry-correlated binding agent into the bloodstream willresult in delivery of the treatment agent (e.g., radionuclides) to thetarget-related tissue ancestry-correlated binding site (e.g., APP in thevicinity of the lung 108). That is, as the target-related tissueancestry-correlated binding agent moves through the bloodstream, thetarget-related tissue ancestry-correlated binding agent will bind withthe target-related tissue ancestry-correlated binding site in thevicinity of the, in this example, diseased tissue 114, thus resulting ineffective application of the attached treatment agent in the desiredregion of the body of the patient 106.

In many cases, delivery of the treatment agent(s) to the vicinity ofdesired body regions, by delivering the treatment agents to definedsites along a blood vessel wall(s) in the desired vicinity, may besufficient to obtain a desired result, even if the treatment agents arecontinually contained within the blood vessel(s) at the target-relatedtissue ancestry-correlated binding sites. In various cases, treatmentagent delivery should occur with greater or lesser levels of specificityand/or efficacy. For example, in some cases, it may be sufficient toprovide the treatment agent in the lung 108, while in other cases thetreatment agent must or should be applied substantially only to thediseased tissue 114.

Additionally, in some cases, it may be possible and/or desirable tobreach or penetrate a wall of the blood vessel(s) 116/120, in order toreach associated tissue(s) directly. For example, in FIG. 1, an enlargedview 118 a of the endothelial layer 118 is illustrated that includes amechanism by which the treatment agents may directly access a direct endtarget of tissue (e.g., the diseased tissue 114). Specifically, FIG. 1illustrates a mechanism 124 that may include, for example, structuresknown as caveolae. Although the mechanism (e.g., caveolae) 124 are shownconceptually in FIG. 1 as tubes or access points, caveolae generallyrefer to small invaginations of a surface of the blood vessel 116 thatcarry out certain transport and/or signaling functions between cellswithin the blood vessel 116 and cells outside of the blood vessel 116(e.g., the diseased tissue 114). Further discussion regarding caveolae124 is provided in various examples, herein.

Although many other examples are provided herein and with reference tothe various figures, it should be understood that many types andinstances of treatment data may play a role in the use and applicationof the various concepts referenced above and described in more detailherein. The treatment system 102 may store such treatment data 126 in adatabase or other memory, for easy, convenient, and effective access bythe clinician 104.

The treatment data 126 may include, for example, not only thetarget-related tissue ancestry-correlated binding site(s) and/or therelated target-related tissue ancestry-correlated binding agent(s), butalso various other parameters and/or characteristics related totreatment of the patient 106, examples of which are provided herein.Through detailed storage, organization, and use of the treatment data126, the clinician 104 may be assisted in determining optimal treatmenttechniques for the patient 106, in order, for example, to select anddeliver an appropriate type and/or level of a treatment agent, with anappropriate degree of accuracy, to a desired end target (based on anappropriate target-related tissue ancestry-correlated binding siteand/or an appropriate target-related tissue ancestry-correlated bindingagent), while minimizing any negative impact of such aselection/delivery, if any, on other regions of the body of the patient106. Ordered assignment and/or storage of information within thetreatment data 126, as described herein, facilitates and/or enables suchrecall, access, and/or use of the treatment data by the clinician 104 intreating the patient 106.

In the treatment system 102, treatment logic 128 may be used to store,organize, access, recall, or otherwise use the information stored in thetreatment data 126. For example, the treatment logic 128 may access adatabase management system (DBMS) engine 130, which may be operable toperform computing operations to insert or modify new data into/withinthe treatment data 126, perhaps in response to new research or findings,or in response to a preference of the clinician 104. For example, if anew treatment agent is discovered to be effective on the diseased tissue114, the clinician 104 may access the treatment system 102 using a userinterface 132, in order to use the DBMS engine 130 to associate the newtreatment agent with one or more instances of the target-related tissueancestry-correlated binding site(s) and/or target related tissueancestry-correlated binding agent(s) that may be known to be useful intargeting the diseased tissue 114, within the treatment data database126 (assuming that the treatment agent is suitable for direct orindirect delivery via the target-related tissue ancestry-correlatedbinding agent, as described herein). As another example, if a newtarget-related tissue ancestry-correlated binding site is identified inthe endothelial layer 118 in the vicinity of the diseased tissue 114,then this new target-related tissue ancestry-correlated binding site maybe associated with one or more instances of a target-related tissueancestry-correlated binding agent, e.g., there may be more than oneagent that is useful in attaching to the new target-related tissueancestry-correlated binding site for delivery of one or more treatmentagents.

Similarly, in a case where the clinician 104 seeks, for example, todiagnose or treat the patient 106, the clinician 104 may access the userinterface 132 to use the treatment logic 128 and/or the DBMS Engine 130to determine best known methods or treatments to be applied in a givenclinical scenario. For example, if the patient 106 has a certain type ofdisease or illness in a certain region of the body, then the clinicianmay input this information via the user interface 132 in order to obtainone or more options for treating the disease or illness. For example, ifthe patient 106 exhibits the diseased tissue 114, then the clinician 104may select the (type of) diseased tissue 114 in the lung 108 as an endtarget, and the treatment logic 128 may then interface with the DBMSengine 130 to obtain, from the treatment data 126, one or more optionsfor providing the treatment agent to the diseased tissue 114, e.g., oneor more target-related tissue ancestry-correlated binding sites (suchas, for example, two different proteins that are expressed or displayedin the endothelial layer 118 in the vicinity of the diseased tissue114). As another example, if the clinician 104 is already aware of atarget-related tissue ancestry-correlated binding site in the vicinityof the diseased tissue 114, then the clinician 104 may input thisinformation into the treatment system 102 and be provided with one ormore, for example, target-related tissue ancestry-correlated bindingagents that may be known to attach to the known target-related tissueancestry-correlated binding site.

In this regard, it should be understood that multiple instances of atarget-related tissue ancestry-correlated binding site, as described,may be present at any one location in the body, and, moreover, virtuallyany region or site in the body having a blood-tissue interface may alsoexhibit an associated, target-related tissue ancestry-correlated bindingsite. Further, new instances of target-related tissueancestry-correlated binding sites may be discovered and/or approved forclinical use on a relatively frequent basis. Still further, there may bemany different treatment parameters and/or characteristics that may berelated to the various target-related tissue ancestry-correlated bindingsite(s) and/or target-related tissue ancestry-correlated bindingagent(s), such as, for example, treatment agents and/or deliverymechanisms.

As a result, the clinician 104, e.g., a physician in the field, may notbe aware of all currently-available content of the treatment data 126.Thus, the treatment system 102 provides the clinician withreadily-available, accurate, current, and/or comprehensive treatmentinformation, and also provides techniques to ensure that the treatmentinformation remains accurate, current, and/or comprehensive, by allowingthe addition and/or modification of the existing treatment data 126, asnew treatment information becomes available.

In FIG. 1, the treatment system 102 is illustrated as possibly beingincluded within a device 134. The device 134 may include, for example, amobile computing device, such as a personal digital assistant (PDA), ora laptop computer. Of course, virtually any other computing device maybe used to implement the treatment system 102, such as, for example, aworkstation, a desktop computer, or a tablet PC.

Additionally, not all of the treatment system 102 need be implemented ona single computing device. For example, the treatment data 126 may bestored on a remote computer, while the user interface 132 and/ortreatment logic 128 are implemented on a local computer. Further,aspects of the treatment system 102 may be implemented in differentcombinations and implementations than that shown in FIG. 1. For example,functionality of the DBMS engine 130 may be incorporated into thetreatment logic 128 and/or the treatment data 126.

The treatment data 126 may be stored in virtually any type of memorythat is able to store and/or provide access to information in, forexample, a one-to-many, many-to-one, and/or many-to-many relationship.Such a memory may include, for example, a relational database and/or anobject-oriented database, examples of which are provided in more detailherein.

FIG. 2 illustrates certain alternative embodiments of the clinicalsystem 100 of FIG. 1. In FIG. 2, the clinician 104 uses the userinterface 132 to interact with the treatment system 102 deployed on theclinician device 134. The clinician device 134 is in communication overa network 202 with a data management system 204, which is also mimingthe treatment system 102; the data management system 204 may beinteracted with by a data manager 206 through a user interface 208. Ofcourse, it should be understood that there may be many clinicians otherthen the specifically-illustrated clinician 104, each with access to anindividual implementation of the treatment system 102. Similarly,multiple data management systems 204 may be implemented.

In this way, the clinician 104, who may be operating in the field, e.g.,in an office and/or hospital environment, may be relieved of aresponsibility to update or manage contents in the treatment data 126,or other aspects of the treatment system 102. For example, the datamanagement system 204 may be a centralized system that manages a centraldatabase of the treatment data 126, and/or that deploys or suppliesupdated information from such a central database to the clinician device134.

FIG. 3 illustrates an alternative embodiment of the treatment data 126associated with the clinical system 100 of FIG. 1. In FIG. 3, and in thevarious examples herein, a particular nomenclature is used for the termsdescribed above and related terms, in order to provide consistency andclarity of description. However, it should be understood that otherterminology may be used to refer to the same or similar concepts.

In FIG. 3, treatment parameters 302 are stored and organized withrespect to a plurality of treatment characteristics 304. The treatmentcharacteristics 304 include many of the terms and concepts justdescribed, as well as additional, but not exhaustive, terms and conceptsthat may be relevant to a use and operation of the treatment system 102.

For example, the treatment characteristics 304 include a direct endtarget 306. The direct end target 306 may refer, for example, to anytissue, organ, organ system, organ subsystem (or type thereof), or anyother body part or region that may be targeted for healing, destruction,repair, enhancement, and/or imaging that may be targeted—directly orindirectly—via an associated target-related tissue ancestry-correlatedbinding site 314 and/or an associated tissue related tissueancestry-correlated binding agent 316 and/or an associated treatmentagent delivery mechanism relative to the target-related tissueancestry-correlated binding agent 318 and/or an associated treatmentagent 320. A discriminated end target 308 refers to targets that shouldbe avoided during implementation of the healing, destruction, repair,enhancement and/or imaging actions that may be discriminated—directly orindirectly—via an associated target-related tissue ancestry-correlatedbinding site 314 and/or an associated tissue related tissueancestry-correlated binding agent 316 and/or an associated treatmentagent delivery mechanism relative to the target-related tissueancestry-correlated binding agent 318 and/or an associated treatmentagent 320. For example, in FIG. 1, the lung 108 may include the directend target 306 as the diseased tissue 114, and may include thediscriminated end target 308 as the healthy tissue 112, and/or thepancreas 110.

Somewhat analogously, a direct intermediate target 310 refers to targetsthat are connected to, associated with, or in the vicinity of the directend target that may be targeted via an associated target-related tissueancestry-correlated binding site 314 and/or an associated tissue relatedtissue ancestry-correlated binding agent 316 and/or an associatedtreatment agent delivery mechanism relative to the target-related tissueancestry-correlated binding agent 318 and/or an associated treatmentagent 320. For example, a portion of the endothelial layer 118 in avicinity of the diseased tissue 114 (or other end target) may act as adirect intermediate target 310. Then, a discriminated intermediatetarget 312 may refer to endothelial tissue of the layer 118 that is notin a vicinity of the diseased tissue 114 that may be discriminated viaan associated target-related tissue ancestry-correlated binding site 314and/or an associated tissue related tissue ancestry-correlated bindingagent 316 and/or an associated treatment agent delivery mechanismrelative to the target-related tissue ancestry-correlated binding agent318 and/or an associated treatment agent 320.

As already referenced, a target-related tissue ancestry-correlatedbinding site 314 refers to a determined chemical and/or genetic and/orbiological structure to which various chemical compounds and/or genesmay be affixed. For example, the target-related tissueancestry-correlated binding site 314 may include a specific protein thatis displayed at the endothelial layer 118 in a vicinity of the diseasedtissue 114. The target-related tissue ancestry-correlated binding site314 may be selectively associated with the direct end target 306 eitherdirectly or through the direct intermediate target 310.

A target-related tissue ancestry-correlated binding agent 316, then, mayrefer to some specific chemical and/or genetic and/or biologicalstructure that more or less selectively binds or attaches to a relatedone of the target-related tissue ancestry-correlated binding sites 314.The target-related tissue ancestry-correlated binding agent 316 also maybe associated with a treatment agent delivery mechanism relative to thetarget-related tissue ancestry-correlated binding agent 318, which mayrefer either to something that may be directly attached to (orassociated with) the target-related tissue ancestry-correlated bindingagent 316, and/or something that may be attached to (or associated with)one or more intermediary or indirect structures that attach to thetarget-related tissue ancestry-correlated binding agent 316 and that actto house and/or deliver a treatment agent 320. As an example of theintermediary or indirect structures just referenced, a nano-containermay be used that dissolves and/or otherwise opens in a vicinity of thetarget-related tissue ancestry-correlated binding site 314, and therebyreleases and/or delivers the treatment agent 320 included inside.

The treatment agent 320 thus binds/attaches to, or otherwise isassociated with, either directly or indirectly, the target-relatedtissue ancestry-correlated binding agent 316. Thus, as described, thetreatment agent 320 may be effectively transported to the appropriatedirect intermediate target 310 and thereby to the target-related tissueancestry-correlated binding site 314. In this way, the treatment agent320 may be delivered to the direct end target 306 (or at least to avicinity of the direct end target 306), while not being delivered eitherto the discriminated intermediate target(s) 312 and/or the discriminatedend target(s) 308.

FIG. 3 thus illustrates that there may be many different relationshipsor associations between any one (or more) of the treatmentcharacteristics 304. For example, one or more instances of any one ormore of the treatment characteristics 304 may be considered to be one ofthe treatment parameters 302, and thereafter associated with one or moreinstances of the remaining treatment characteristics 304. For example,the direct end target 306 may be considered to be the treatmentparameter(s) 302, where a first instance 302 a of the direct end target306 may refer to diseased lung tissue, and the second instance 302 b mayrefer to diseased breast tissue, and both instances may be associatedwith an instance of the target-related tissue ancestry-correlatedbinding agent 316. Similarly, two or more instances of thetarget-related tissue ancestry-correlated binding agent 316 (e.g.,I-labeled APP monoclonal antibodies targeted on two different antigens)may be associated with one treatment agent 320 (e.g.,radio-immunotherapy via application of low levels of radionuclides).

Many other examples of relationships and associations between thevarious treatment parameters 302 and/or the treatment characteristics304 (and/or other treatment information) may be defined or determinedand stored in the treatment data 126 according to the treatment logic128. Certain of these examples are provided herein.

Additionally, although the treatment data 126 is illustratedconceptually in FIG. 3 as a flat table in which one or more of theselected treatment parameters 302 are associated with one or more of thetreatment characteristics, it should be understood that thisillustration is for explanation and example only, and is not intended tobe limiting in any way with respect to the various ways in which thetreatment data 126 may be stored, organized, accessed, recalled, orotherwise used.

For example, the treatment data 126 may be organized into one or morerelational databases. In this case, for example, the treatment data 126may be stored in one or more tables, and the tables may be joined and/orcross-referenced in order to allow efficient access to the informationcontained therein. Thus, the treatment parameter(s) 302 may define arecord of the database(s) that is associated with various ones of thetreatment characteristics 304.

In such cases, the various tables may be normalized so as, for example,to reduce or eliminate data anomalies. For example, the tables may benormalized to avoid update anomalies (in which the same informationwould need to be changed in multiple records, and which may beparticularly problematic when treatment data database 126 is large),deletion anomalies (in which deletion of a desired field or datumnecessarily but undesirably results in deletion of a related datum),and/or insertion anomalies (in which insertion of a row in a tablecreates an inconsistency with another row(s)). During normalization, anoverall schema of the database may be analyzed to determine issues suchas, for example, the various anomalies just referenced, and then theschema is decomposed into smaller, related schemas that do not have suchanomalies or other faults. Such normalization processes may be dependenton, for example, desired schema(s) or relations between the treatmentparameters 302 and/or treatment characteristics 304, and/or on desireduses of the treatment data 126.

Uniqueness of any one record in a relational database holding thetreatment data 126 may be ensured by providing or selecting a column ofeach table that has a unique value within the relational database as awhole. Such unique values may be known as primary keys. These primarykeys serve not only as the basis for ensuring uniqueness of each row(e.g., treatment parameter) in the database, but also as the basis forrelating or associating the various tables within one another. In thelatter regard, when a field in one of the relational tables matches aprimary key in another relational table, then the field may be referredto a foreign key, and such a foreign key may be used to match, join, orotherwise associate (aspects of) the two or more related tables.

FIG. 3 and associated potential relational databases represent only oneexample of how the treatment data may be stored, organized, processed,accessed, recalled, and/or otherwise used.

FIG. 4 illustrates another alternative embodiment of treatment data 126associated with the clinical system 100 of FIG. 1, in which thetreatment data 126 is conceptually illustrated as being stored in anobject-oriented database.

In such an object-oriented database, the various treatment parameter(s)302 and/or treatment characteristic(s) 304, and/or instances thereof,may be related to one another using, for example, links or pointers toone another. FIG. 4 illustrates a conceptualization of such a databasestructure in which the various types of treatment data areinterconnected, and is not necessarily intended to represent an actualimplementation of an organization of the treatment data 126.

The concepts described above may be implemented in the context of theobject-oriented database of FIG. 4. For example, two instances 320 a and320 b of the treatment agent 320 may be associated with one (or more)instance 316 a of the target-related tissue ancestry-correlated bindingagent 316. Meanwhile, two instances 316 a and 316 b of thetarget-related tissue ancestry-correlated binding agent 316 may beassociated with an instance 314 a of the target-related tissueancestry-correlated binding site 314.

Also, other data may be included in the treatment data 126. For example,in FIG. 4, a treatment agent precursor 402 is shown that refersgenerally to an agent used to facilitate application of the treatmentagent 320, e.g., an immune-response element that is used toidentify/mark/bond with the target-related tissue ancestry-correlatedbinding site 314 and/or a substance that when metabolized becomestreatment agent 320, such as with prodrugs.

Many other examples of databases and database structures also may beused. Other such examples include hierarchical models (in which data areorganized in a tree and/or parent-child node structure), network models(based on set theory, and in which multi-parent structures per childnode are supported), or object/relational models (combining therelational model with the object-oriented model).

Still other examples include various types of eXtensible Mark-upLanguage (XML) databases. For example, a database may be included thatholds data in some format other than XML, but that is associated with anXML interface for accessing the database using XML. As another example,a database may store XML data directly. Additionally, or alternatively,virtually any semi-structured database may be used, so that context maybe provided to/associated with stored data elements (either encoded withthe data elements, or encoded externally to the data elements), so thatdata storage and/or access may be facilitated.

Such databases, and/or other memory storage techniques, may be writtenand/or implemented using various programming or coding languages. Forexample, object-oriented database management systems may be written inprogramming languages such as, for example, C++ or Java. Relationaland/or object/relational models may make use of database languages, suchas, for example, the structured query language (SQL), which may be used,for example, for interactive queries for information and/or forgathering and/or compiling data from the relational database(s).

As referenced herein, the treatment system 102 may be used to performvarious data querying and/or recall techniques with respect to thetreatment data 126, in order to facilitate treatment and/or diagnosis ofthe patient 106. For example, where the treatment data are organized,keyed to, and/or otherwise accessible using one or more of the treatmentparameters 302 and/or treatment characteristics 304, various Boolean,statistical, and/or semi-Boolean searching techniques may be performed.

For example, SQL or SQL-like operations over one or more of thetreatment parameters 302/treatment characteristics 304 may be performed,or Boolean operations using the treatment parameters 302/treatmentcharacteristics 304 may be performed. For example, weighted Booleanoperations may be performed in which different weights or priorities areassigned to one or more of the treatment parameters 302/treatmentcharacteristics 304, perhaps relative to one another. For example, anumber-weighted, exclusive-OR operation may be performed to requestspecific weightings of desired (or undesired) treatment data to beincluded (excluded).

For example, the clinician 104 may wish to determine examples of thedirect end target 306 that are associated with examples of thediscriminated end target 308 that are highly discriminated against withrespect to delivery of the target-related tissue ancestry-correlatedbinding agent 316, for highly-specific delivery of the treatment agent320. For example, the clinician 104 may want to know instances of thetreatment agent 320 that may be delivered to the lungs as the direct endtarget 306, without substantially affecting the pancreas, liver, orother tissue, organ, or organ system/subsystem. In other examples, theclinician may be willing to tolerate lower levels of discrimination(e.g., increased delivery of the treatment agent 320 to other bodyregions), perhaps because the patient 106 is in an advanced stage ofillness. As another example, the clinician 104 may start with apreferred (type of) the treatment agent 320, and may request from thetreatment system 102 various delivery techniques (e.g., target-relatedtissue ancestry-correlated binding agent 316) that may be available,perhaps with varying levels of efficacy.

The clinician 104 may specify such factors using, for example, the userinterface 132. For example, the clinician 104 may be able to designateone or more of the treatment parameters 302/treatment characteristics304, and assign a weight or importance thereto, using, for example, aprovided ranking system. In this regard, and as referenced herein, itshould be understood that the clinician 104 may wish to deliver aparticular instance of the treatment agent 320, e.g., a particularradionuclide to be delivered to a tumor. However, such a treatmentagent, if applied by conventional techniques, may be problematic orprohibited (e.g., where a current physiological condition of the patient106 and/or state of an immune system of the patient 106 is insufficientto allow the clinician 104 to use the desired treatment agent).Moreover, the clinician 104 may not be aware that a suitabletarget-related tissue ancestry-correlated binding site 314 and/ortarget-related tissue ancestry-correlated binding agent 316 has (have)been discovered for delivering the treatment agent with adesired/required level of accuracy. However, the clinician 104 may querythe treatment system 102 based on the desired treatment agent 320, andmay thereby discover the technique(s) by which the treatment agent maybe applied, and with the necessary level of specificity.

Similarly, data analysis techniques (e.g., data searching) may beperformed using the treatment data 126, perhaps over a large number ofdatabases. For example, the clinician 104 may perform a physicalscreening of the patient 106, and may input some body system, tissue,organ, or organ system/subsystem parameters against which screening isto be performed. Then, the clinician should receive a listing oftarget-related tissue ancestry-correlated binding sites and/ortarget-related tissue ancestry-correlated binding agents that are rankedaccording to some criteria. For example, the clinician 104 may receive alisting of instances of the target-related tissue ancestry-correlatedbinding site 314 that provide a particularly high or low level ofdiscrimination with respect to a particular direct end target 306,discriminated end target 308, and/or treatment agent 320. In this way,for example, if the patient 106 has an organ or organ subsystem thatrequires protection from a given instance of the treatment agent 320,then the clinician 104 may select an instance of the target-relatedtissue ancestry-correlated binding site 314 and/or of the target-relatedtissue ancestry-correlated binding agent 316 accordingly, even if somerelative sacrifice of binding strength/accuracy is associated with sucha selection.

By way of further example, other parameters/characteristics may befactored in. For example, elimination pathways may be tracked,databased, and/or weighted for use in the treatment data 126 and/or thetreatment system 102. For example, if a particular instance of thetarget-related tissue ancestry-correlated binding agent is especiallyreadily eliminated by the liver, then, in a case where the patient 106has impaired hepatic function, such an instance may be selected fordelivering the treatment agent 320, even if an otherwise superiorinstance of the target-related tissue ancestry-correlated binding agent316 is known. Algorithms implementing such query/recall/access/searchingtechniques may thus use Boolean or other techniques to output, forexample, a thresholded, rank-ordered list. The treatment logic 128 maythen assign a key or other identifier to such a list(s), for easier usethereof the next time a like query is performed.

Design and testing of querying techniques in particular implementationsof the treatment system 102 may involve, for example, entry of candidatetreatment parameters 302/treatment characteristics 304 (or instancesthereof) into a database(s), along with associated test results and/oraffinity metrics that may be used to determine/weight targets or sets oftargets. Then, an identifier may be generated that is unique to thetarget(s) set(s).

Still other examples/applications include avoiding an auto-immuneresponse of the patient 106, in order to achieve a desired result. Forexample, the treatment system 102 may be used to determine/catalog/usetreatment data that relates to treatment parameters 302/treatmentcharacteristics 304 that are known or suspected to avoid self-epitopes(i.e., those unlikely to generate an undesired autoimmune response).FIG. 5 illustrates another alternative embodiment of treatment dataassociated with the clinical system 100 of FIG. 1, with specificexamples of treatment data. In particular, all of FIGS. 5-7 provide orrefer to example results from related technical papers, which arespecifically referenced below.

For example, rows of the table of FIG. 5 (e.g., rows 502, 504, and 506,respectively) refer to examples that may be found in Oh, P. et al.,“Subtractive Proteomic Mapping of the Endothelial Surface in Lung andSolid Tumours for Tissue-Specific Therapy,” Nature, vol. 429, pp.629-635 (Jun. 10, 2004), which is hereby incorporated by reference inits entirety, and which may be referred to herein as the Oh reference.

In the Oh reference, it is generally disclosed that regions ofendothelium may change or alter over time, based on what tissues are inthe vicinity thereof, as referenced herein. The Oh reference, forexample, identified lung-induced and/or lung-specific endothelial cellsurface proteins based on a hypothesis that a surrounding tissue (micro)environment of the endothelial cell surface proteins modulates proteinexpression in the vascular endothelium. The Oh reference identifiedspecific proteins that were found to be expressed at an endothelialsurface by specifying two regions of interest (e.g., a “lung region” anda “non-lung region”), and then determining proteins within the tworegions. Then, by subtracting the two sets of proteins from one another,non-common proteins were identified.

In this way, uniquely occurring proteins at a specific endothelial site(e.g., the target-related tissue ancestry-correlated binding site 314 ata specific direct intermediate target 310) were identified. Then, theseuniquely-occurring proteins were used as targets for generatedantibodies. As a result, it was possible to target, for example,lung-specific tissues as opposed to non-lung-specific tissues, and/or totarget tumors as opposed to non-tumor tissues. More specifically, forexample, it was determined to be possible to target tumor-inducedendothelial cell proteins (e.g., target-related tissueancestry-correlated binding sites 314) for delivery thereto of drugs,imaging agents, and/or radiation agents (e.g., treatment agents 320)that were attached to appropriate antibodies (target-related tissueancestry-correlated binding agents 316).

Thus, to set forth specific examples, a row 502 illustrates an examplein which the direct end target 306 includes a treatment parameter of“lung tissue.” In this example, the discriminated end target 308includes “non-lung tissue.” The direct intermediate target 310 includesendothelial tissues that are proximate to the lung tissue, while thediscriminated intermediate target 312 includes endothelial tissue thatis proximate to the non-lung tissue.

The target-related tissue ancestry-correlated binding site 314 in thisexample includes aminopeptidase-P (APP), which is a protein that wasdetected substantially only in endothelial plasma membranes from thelung tissue (e.g., direct end target 306). In order to take advantage ofthe immuno-accessibility of APP in vivo, I¹²⁵-labeled monoclonalantibodies were used as the target-related tissue ancestry-correlatedbinding agent 316, and were intravenously injected into test rats.Subsequent imaging of the lungs illustrated rapid and specific targetingof APP antibody to the lung (e.g., direct end target 306), withsignificantly reduced accumulation of the injected dose at non-lungtissue (e.g., the discriminated end target 308). Thus, by selecting thetreatment agent 320 to include radio-immunotherapy via low levels ofradionuclides (e.g., 100 μCi of I¹²⁵), a treatment agent deliverymechanism relative to target-related tissue ancestry-correlated bindingagent 318 may involve essentially direct delivery, in that theradionuclide(s) may be affixed to the monoclonal APP antibodies,similarly to how the I¹²⁵ was affixed as described in Oh, et al.Further, although the term antibody is used herein in various examples,it should be understood that other immuno-reactive features of theadaptive immune system also may be used in a similar or analogousmanner, including entities that serve to mediate antibody generation,such as, for example, helper T cells or dendritic cells.

In the row 504 of FIG. 5, a conceptual secondary example drawnfrom/based on the Oh reference is included, in order to illustratevarious concepts described herein, e.g., with respect to FIGS. 1-4.Specifically, in the row 504, various ones of the treatment parametersand/or treatment characteristics are the same as in the row 502, exceptthat a second example of the target-related tissue ancestry-correlatedbinding agent 316 is illustrated generically as “Binding Agent X,” and,similarly, a second example of a generically-referenced treatment agent320 is illustrated as “Treatment Agent X.” As such, the row 504illustrates, for example, that two separate instances of thetarget-related tissue ancestry-correlated binding agent 316 and/or thetreatment agent 320 may be associated with, e.g., an instance of eitherthe direct end target 306, and/or with an instance of the target-relatedtissue ancestry-correlated binding site 314.

The row 506 illustrates another example from the Oh reference. In therow 506, the direct end target 306 is illustrated as “diseased lungtissue,” while the discriminated end target 308 is illustrated as“non-diseased lung tissue.” Thus, the direct intermediate target 310 isillustrated as “endothelial tissue proximate to the diseased lungtissue,” while the discriminated intermediate target 312 is illustratedas “endothelial tissue that is proximate to non-diseased lung tissue.”

Then, the target-related tissue ancestry-correlated binding site 314 isillustrated as fifteen differentially-expressed proteins (e.g.,expressed according to the subtractive techniques described herein)associated with the direct intermediate target 310, e.g., theendothelial tissue proximate to the diseased lung tissue. As a result,the target-related tissue ancestry-correlated binding agent 316 isselected and illustrated as I-labeled monoclonal APP antibodies that maybe generated for one or more of the fifteen differentially-expressedproteins. As in the row 502, the treatment agent delivery mechanismrelative to target-related tissue ancestry-correlated binding agent 318may involve essentially direct attachment of the treatment agent 320that is illustrated as radio-immunotherapy via low-levels ofradionuclides. In this way, such radionuclides may be concentrated in,and may thereby destroy, tumors. In particular, for example, anidentified tumor target was the 34 KDa protein recognized by annexin A1(AnnA1) antibodies, which was significantly present in substantiallyonly in tumor endothelial plasma membrane.

FIG. 6 illustrates additional alternative embodiments of treatment dataassociated with the clinical system 100 of FIG. 1, with specificexamples of treatment data. In FIG. 6, a row 602 illustrates examplesthat may be found in Essler et al., “Molecular Specialization of BreastVasculature: A Breast-Homing Phage-Displayed Peptide Binds toAminopeptidase P in Breast Vasculature,” Proceedings of the NationalAcademy of Sciences, vol. 99, No. 4, pp. 2252-2257 (Feb. 19, 2002),which is hereby incorporated by reference in its entirety, and which maybe referred to herein as the Essler reference.

In the Essler reference, a plurality of peptides (e.g., two or moreamino acids joined together via a peptide bond) having a generalstructure of CX7C (where C is cysteine and X is any amino acid)I-labeled monoclonal antibodies were injected into mice. Then tissues ofinterest were observed to determine a presence of phage(s), and therebyto determine which peptide of the plurality of peptides honed in on theobserved tissue(s). In this way, it was determined that the CPGPEGAGCpeptide was useful in providing a horning point for phages of thepatient's immune system, and, in particular, was useful as a bindingagent for the breast tissue, while not binding to pancreas tissue.Although these specific examples of peptides are provided forillustration and explanation, it should be understood that the termpeptide as used herein may refer to virtually any lineal peptide-bondedstring of amino acid residues, which include various structures thereof,unless context dictates otherwise. For example, a lipopeptide may beinterpreted to include virtually all lipoproteins, while glycopeptidesmay include virtually all glycoproteins.

Thus, in the row 602, the direct end target 306 is illustrated as breasttissue, while the discriminated end target 308 is illustrated aspancreas tissue. The direct intermediate target 310 is illustrated asvascular beds of breast tissue, while the discriminated intermediatetarget 312 is illustrated as vascular beds of pancreas tissue.

The target-related tissue ancestry-correlated binding site 314 includesa protein, aminopeptidase-P (APP), of the vascular bed of breast tissue.The target-related tissue ancestry-correlated binding agent 316 includesa cyclic nonapeptide known as the CPGPEGAGC peptide, which is shown inthe Essler paper to home to the aminopeptidase P receptor. The treatmentagent precursor 402 is shown to include phages, which were essentiallydirectly delivered via the CPGPEGAGC peptide to the APP of the vascularbed of breast tissue, and which facilitate attachment ofadditional/alternative treatment agents 320 to the APP.

A row 604 of FIG. 6 illustrates an example from Hood et al., “TumorRegression by Targeted Gene Delivery to the Neovasculature,” Science,vol. 296, pp. 2404-2407 (Jun. 28, 2002), which is incorporated byreference in its entirety and which is referred to herein as the Hoodreference. The Hood reference refers to the molecule intergin avB3 thatplays a role in endothelial cell survival during formation of new bloodvessels in a given region, and is preferentially expressed therein. Acationic polymerized lipid-based nanoparticle was synthesized andcovalently coupled to a small organic avB3 ligand; that is, the ligandwas demonstrated to serve as a binding agent for the integrin avB3 thatis preferentially expressed in endothelial cells.

Accordingly, in the row 604, melanoma tumors were used as the direct endtarget 306, while the discriminated end target 308 is shown assurrounding non-tumor tissues. The direct intermediate target 310 isillustrated as endothelial cells having integrin avB3, while thediscriminated intermediate target 312 is shown as endothelial cellswithout integrin avB3. Thus, the target-related tissueancestry-correlated binding site 314 is shown to include the integrinavB3, while the target-related tissue ancestry-correlated binding agent316 is shown to include the avB3 ligand that attaches to the integrinavB3. The treatment agent 320 included a gene selected to disruptformation of new blood vessels in the tumor(s), which was deliveredusing the cationic polymerized lipid-based nanoparticle(s), and whichthereby deprived the tumor(s) of blood and destroyed the tumor(s).

FIG. 7 illustrates additional embodiments of treatment data associatedwith the clinical system 100 of FIG. 1, with specific examples oftreatment data. In a row 702, an example is illustrated from McIntosh etal., “Targeting Endothelium and Its Dynamic Caveolae for Tissue-SpecificTranscytosis in vivo: A Pathway to Overcome Cell Barriers to Drug andGene Delivery,” Proceedings of the National Academy of Sciences, vol.99, no. 4, pp. 1996-2001 (Feb. 19, 2002), which is hereby incorporatedby reference and which may be referred to herein as the McIntoshreference. In the McIntosh reference, endothelial cell plasma membranesfrom the lungs were analyzed to determine monoclonal antibodies targetedthereto. Additionally, the McIntosh reference illustrated use of thecaveolae 124 to allow the treatment agent 320 to cross the endotheliumand be delivered directly to lung tissue.

Thus, in the row 702, the direct end target 306 is shown as lung tissue,while the discriminated end target 308 is shown as non-lung tissue. Thedirect intermediate target 310 is shown as endothelial cell caveolaeproximate to the lung tissue, while the discriminated intermediatetarget 312 is shown as endothelial cell caveolae that is distal from thelung tissue.

The target-related tissue ancestry-correlated binding site 314 is shownas a determined/selected antigen to which the monoclonal antibodyTX3.833 binds, so that the target-related tissue ancestry-correlatedbinding agent 316 is shown as the monoclonal antibody TX3.833 itself. Inthis way, the treatment agent 320 of gold affixed directly to theTX3.833 antibody was transported over the endothelial plasma membraneinto the tissues of interest (e.g., lung tissues); in other words, thecaveolae 124 was used to conduct transcytosis.

A row 704 illustrates an example from Zhiwei et al., “Targeting TissueFactor on Tumor Vascular Endothelial Cells and Tumor Cells forImmunotherapy in Mouse Models of Prostatic Cancer,” Proceedings of theNational Academy of Sciences, vol. 98, no. 21, pp. 12180-12185 (Oct. 9,2001), which is hereby incorporated by reference in its entirety, andwhich may be referred to as the Zhiwei reference. In the Zhiweireference, a “tissue factor” is identified as a transmembrane receptorthat forms a strong and specific complex with an associated ligand,factor VII (fVII). Such tissue factor, although not normally expressedon endothelial cells, may be expressed on tumor endothelial cells of thetumor vasculature.

Thus, in the example of the row 704, the direct end target 306 includesprostrate tumors, while the discriminated end target 308 includes allother tissues. The direct intermediate target 310 includes tissuefactor(s) expressed by/on endothelial cells near the tumor(s) and by/onthe tumor itself. The target-related tissue ancestry-correlated bindingsite 314 includes the tissue factor, while the target-related tissueancestry-correlated binding site agent 316 includes the factor VII(fVII), the ligand for the tissue factor. In this way, the directtreatment agent 320 of a Fc effector domain was used to provide a markerfor an induced immune response.

In a row 706, an example is illustrated from Kaplan et al.,“VEGFR1-positive haematopoietic bone marrow progenitors initiate thepre-metastatic niche,” Nature, vol. 438, no. 4, pp. 820-827 (December2005), which is hereby incorporated by reference and which may bereferred to herein as the Kaplan reference. In the Kaplan reference,metastasis is described as a process in which tumor cells mobilizebone-marrow cells to form a site or “pre-metastatic niche” at particularregions (distant from the primary tumor itself), at which the subsequentmetastasis may then develop. More specifically, Kaplan describes theidea that cells of a tumor may secrete a molecular/humoral factor(s)that mobilizes bone marrow cells and stimulates fibroblast cells at adistant (future metastatic) site, thereby upregulating fibronectin (abinding, tissue-promoting protein) that serves as a “docking site” forthe bone marrow cells. Some of the bone marrow cells were positive forproteins characteristic of haematopoietic progenitor cells, including,for example, vascular endothelial growth factor receptor 1 (VEGFR1),which, in turn, is described as promoting attachment and motility oftumor cells, thereby leading to metastasis. For example, proteaseproduction associated with the bone marrow cells may lead to growthfactors (e.g., vascular endothelial growth factor (VEGF) that supportthe developing niche, through, e.g., angiogenesis). In other words, theVEGFR1-positive bone marrow cells serve to form the “pre-metastaticniche” by colonizing a site distant from the tumor, so thatsubsequently-arriving tumor cells find a hospitable environment at sucha site.

Thus, in the example of the row 706, the direct end target 306 mayinclude one-or-more metastatic niches or sites that are distant from aprimary tumor. For example, such niches may be present in the lungs whenthe primary tumor includes a melanoma. Then, the discriminated endtarget 308 may include tissues other than these metastatic niches. Thedirect intermediate target 310 may include endothelial cells at themetastatic niches, while the discriminated intermediate target 312 mayinclude endothelial cells at other locations. In the example of the row706, the target-related tissue ancestry correlated binding site 314includes VEGFR1, which, as referenced above, includes a receptor proteinon the endothelial cells (to which VEGF may bind). In this case, and asreferenced in the Kaplan reference, the target-related tissue ancestrycorrelated binding agent 316 may include an antibody to VEGFR1, so thatthe treatment agent delivery mechanism relative to the target-relatedtissue ancestry correlated binding agent 318 includes an essentiallydirect delivery of this antibody, where the antibody to VEGFR1 therebyserves as the treatment agent 320 by blocking the VEGFR1 and preventingformation of, occupying, and/or blocking subsequent interactions withdevelopment of the pre-metastatic niche. Of course, the row 706 includesmerely one example of target-related tissue ancestry correlated bindingsites) and/or target-related tissue ancestry correlated binding agent(s)that may be located within, or in association with, the pre-metastaticniche(s), where appropriate discovery and/or targeting thereof may beperformed by any of the techniques described herein, or othertechniques. Moreover, it should be understood from the above descriptionthat such target-related tissue ancestry correlated binding site(s)and/or target-related tissue ancestry correlated binding agent(s) may betime-dependent, e.g., with respect to formation and metastasis of theprimary tumor. Accordingly, application of the just-referencedtechniques may be determined and/or occur based on suchtime-dependencies, e.g., by applying the techniques for patients at highrisk of metastatic disease, but for whom metastatic disease has not yetactualized in the form of established metastases.

FIG. 8 illustrates an example screenshot of a graphical user interfacefor accessing predictive data. In FIG. 8, an example of the userinterface 132 of FIG. 1 is illustrated as providing a graphicalillustration 802 of the patient 106. For example, the graphicalillustration 802 may include an image of some or all of the patient 106,where the image may include various colors, highlights, or other visualindicators designed to provide information regarding the patient 106, orregarding a diagnosis or treatment of the patient 106. The graphicalillustration 802 may illustrate internal organs of interest, andsurrounding or related body portions, with varying (and variable) levelsof resolution. For example, user controls (not shown in FIG. 8) may beprovided that allow the clinician 104 to view the graphical illustration802 by zooming in or out, or by moving a viewing focus of/on thegraphical illustration 802. Although illustrated in FIG. 8 as anoutline, the graphical illustration 802 may include other visualrepresentations of the patient 106, which may be generic to a class ofpatient or specific to a particular patient, and which may include aphotograph or other illustration derived from image sensor(s), or athree-dimensional representation of the patient 106. Additionally, oralternatively, the graphical illustration 802 may include a chart,graph, diagram, table, or other representation of data that may beuseful to the clinician 104 in diagnosing or treating the patient 106.

In the example of FIG. 8, the user interface 132 includes a plurality offields 804, 806, 808, 810, 812, and 814. In some implementations, thefields 804-814 allow the clinician to access, analyze, or otherwiseconsider or use the treatment data 126 of FIG. 1 to diagnose and/ortreat the patient 106. For example, as referenced herein, the clinician104 may determine or consider treatment techniques to select and deliveran appropriate type and/or level of a treatment agent, with anappropriate degree of accuracy, to a desired (direct) end target, whileminimizing a negative impact of such a selection/delivery, if any, onother regions of the body of the patient 106. In some implementations,the user interface 132 thus provides the clinician 104 with bases forspeculation or conjecture regarding a potential course of treatment orresearch that may be undertaken with regard to the patient 106. In otherwords, for example, the user interface 132 allows the clinician 104 tohypothesize about an efficacy, risk, unwanted impact, or side effect ofa particular course of treatment that may be undertaken.

For example, the field 804 may include a drop-down menu by which theclinician 104 may select a direct end target that is desired fortreatment or analysis. In the example of FIG. 8, the field 804 isillustrated as showing a selection of “cancer cells in lung” as thedirect end target. Meanwhile, the field 806 illustrates a selection of“radionuclides” as a potential treatment agent.

As described herein, delivery of radionuclides or other appropriatetreatment agents to a desired bodily location may be accomplished byusing a “molecular address” provided by a target-related tissueancestry-correlated binding site, e.g., by associating the treatmentagent (radionuclides) with a target-related tissue ancestry-correlatedbinding agent that is known to deliver the treatment agent to thetarget-related tissue ancestry-correlated binding site (and thereby, forexample, to surrounding target tissue), while discriminating against, oravoiding, ancillary or undesired delivery of the treatment agent tonon-target tissue(s). Thus, in the example of FIG. 8, once the clinician104 selects a desired direct end target using the field 804, and adesired treatment agent in the field 806, then the clinician 104 mayselect “request suggestion” in the field 808 associated with atarget-related tissue ancestry-correlated binding agent, as shown. Inthis case, the system 100 or similar system (e.g., the system 900 ofFIG. 9, discussed in more detail, below) may thus provide a suggestionfor the target-related tissue ancestry-correlated binding agent of “Ilabeled monoclonal antibodies” in the field 810, for consideration andpossible use by the clinician 104 in applying the treatment agent(radionuclides) of the field 806 of the direct end target (cancer cellsin lung) of the field 804.

Of course, FIG. 8 and the above discussion provide merely a few examplesof how the user interface 132 may be used in conjunction with thetreatment logic 128 of the treatment system 102 to access the treatmentdata 126. In other examples, the clinician 104 may request a suggestionfor the direct end target in the field 804, or may request a suggestionfor the treatment agent 806, or, on the other hand, may simply specifyall desired treatment parameters (in which case no suggested treatmentparameter need be provided in the field 810). Further, although FIG. 8is illustrated for the sake of example as including fields for thedirect end target, the treatment agent, and the target-related tissueancestry-correlated binding agent, it should be understood that any ofthe various treatment parameters mentioned herein, or other treatmentparameters, may be selected or provided in conjunction with the userinterface 132.

However the treatment parameter(s) are selected and/or provided in theuser interface 132, the graphical illustration 802 may be used toprovide possible outcomes of a use of the treatment parameter(s) withrespect to one or more body portions. For example, in the illustratedexample of FIG. 8, where the treatment parameters of the fields 804-810are selected or provided, the graphical illustration 802 may be used toillustrate a possible outcome of the use of the treatment parameterswith respect to the lungs 108 and/or the pancreas 110. For example,since cancer cells in the lungs 108 are intended to be used as thedirect end target, as specified in the field 804, the graphicalillustration 802 may be used to illustrate an effect of delivering thespecified treatment agent (radionuclides) of the field 806 to the lungs108, using the target-related tissue ancestry-correlated binding agentsuggested in the field 810 (also using, it will be appreciated, theappropriate target-related tissue ancestry-correlated binding siteassociated with the lungs 108 to which the target-related tissueancestry-correlated binding agent is known to bind). For example, acolor scheme or other visual indicator(s) may be used to indicate anefficacy of the specified treatment parameters with respect to the lungs108, e.g., by providing the illustration of the lungs 108 in differentcolors to indicate the efficacy of the specified treatment parameters.Of course, other audio or visual indicators may be used, e.g., thegraphical illustration 802 may include a brightness or other visualaspect of the illustration of the lungs 108 that is varied in direct orindirect correspondence with an efficacy of the specified treatmentparameters.

As a result, the clinician 104 may, for example, observe and judge anefficacy of a plurality of successively-specified treatment parameters,simply by selecting or requesting examples and combinations thereof,using the fields 804-810. By use in part of such visual indicators asthose just described, the clinician 104 may quickly and easily makejudgments about which treatment parameter(s) may be most useful in agiven diagnostic, treatment, or research scenario.

In some implementations, the graphical illustration 802 may be used toprovide other possible outcomes of the use of the treatmentparameter(s), beyond illustrating an efficacy thereof. For example, thegraphical illustration 802 may automatically illustrate side effects,unwanted impacts, or other risks, ambiguities or consequences of usingthe specified treatment parameter(s). For example, as described herein,it may be the case that use of the specified treatment parameter(s) mayresult in an undesired side effect of, for example, delivery of thetreatment agent (e.g., radionuclides) to other body portions.Accordingly, the graphical illustration 802 may illustrate body portionsthat may be affected by the use of the treatment parameter(s) in anundesired, unwanted, and/or detrimental manner. For example, thegraphical illustration 802 may include a representation of the pancreas110, which may be affected by the treatment agent (radionuclides) in anundesired manner. Again, visual indicators may be used to indicate anature and/or extent of the undesired effect, using, e.g., a designatedcolor scheme, highlighting, numerical or graphical representation, orother visual indications.

Thus, again, the clinician 104 may gain useful information fordiagnosing or treating the patient 106, or for general research/inquiryinto uses of different treatment parameters. For example, by specifyingdifferent (combinations of) treatment parameters, the clinician mayobserve an efficacy of a desired treatment, relative to a nature andextent of unwanted impacts thereof. For example, the clinician 104 maybe reminded (or made aware) of certain side effects that may nototherwise have been considered or known, and may respond accordingly.For example, if the patient 106 is known to have a weakened or somewhatdysfunctional pancreas, then different treatment parameters may beselected to find combinations thereof that retain a desired level ofefficacy, while avoiding dangerous or unwanted application of thetreatment agent to the pancreas 110.

In providing the graphical illustration 802, including possible outcomes(both beneficial and detrimental) of the use of the specified treatmentparameters, the user interface 132 may access and use the treatment data126, using the treatment logic 128. In the example of FIG. 8, thetreatment data 126 may include a plurality of datasets used by thetreatment logic 128 to provide the graphical illustration 802, whereeach dataset may be associated with at least one predictive basis forproviding the possible outcome(s) of the use of the various treatmentparameters.

For example, a first such dataset may be associated with a firstpredictive basis that may include previous studies or trials performedon human subjects. That is, results of previous studies or trialsperformed on human subjects may be stored in the first dataset, andthese results may be tagged, identified, or otherwise characterizedwithin the treatment data 126 as having a certain type or degree ofpredictive value. For example, the first dataset may be characterized asbeing more predictively useful than results from a second datasetassociated with studies or trials based on animals, simply by virtue ofhaving been performed on human subjects. In other examples, the resultsin the first dataset may be characterized as having been performed in acertain timeframe or environment, under certain funding and/orprocedural guidelines, within a defined area or type of medicalpractice, or having some other predictive basis and/or value. In theseand other such examples, the first dataset may be designated to havemore or less predictive value than a second dataset that also storesresults of studies or trials performed on human subjects, but where theidentified characteristic(s) is different in quantity or quality (e.g.,performed in a different timeframe or environment, or under more or lessstringent funding and/or procedural guidelines, or in a different areaof medical practice (e.g., holistic/alternative as compared totraditional)).

In the example of FIG. 8, a field 812 is included that allows theclinician 104 to specify one or more datasets to be used by thetreatment logic 128 in generating the graphical illustration 802. Forexample, the field 812 illustrates that the clinician 104 may select oneor more datasets associated with human studies, animal studies, computersimulations, “in silico” datasets, speculated datasets, or aggregateddatasets (where, for example, the clinician 104 may specify differentcombinations or aggregations of the different datasets, e.g., byselecting multiple ones of the listed examples). Of course, these arejust examples, and any other knowledge source may be used, as would beapparent, including, for example, any type of in vivo or in vitro or insilico study.

In this way, for example, the clinician 104 may use the user interface132 as a convenient tool to perform analysis, speculation, or predictionof a possible outcome of the use of specified treatment parameters,based on the different datasets having different predictive bases. Forexample, for the treatment parameters specified in the fields 804-810,the clinician may first select “human studies” in the field 812,whereupon the user interface 132 may provide the graphical illustration802 with a first illustration of the lungs 108, perhaps in associationwith a certain color or other visual indicator designed to illustrate anefficacy of the treatment parameters with respect to the lungs 108 (or,more specifically, with respect to certain types of cancer cells withinthe lungs 108). In this first example, the pancreas 110 may notinitially be illustrated (or may be illustrated but not visually markedor altered), since, for example, the human studies providing the firstpredictive basis of the first dataset may not have shown any adverseeffects with respect to the pancreas 110.

Then, the clinician may specify a second dataset having a secondpredictive basis, such as, for example, a dataset associated with“animal studies,” as selected from the field 812. In this case, the userinterface 132 may modify the graphical illustration 802 to provide amodified graphical illustration that includes the pancreas 110 (and/or avisual indicator associated therewith), and that thereby illustratesthat the results of the second dataset indicate that a possible outcomeof the use of the specified treatment parameters includes unwantedapplication of the treatment agent to the pancreas 110.

As a result, for example, the clinician 104 may make a more informeddecision about a future course of action regarding a diagnosis ortreatment of the patient 106. For example, the fact that the animalstudies of the second dataset indicate the possible outcome of unwantedimpact on the pancreas 110 may not be considered to be conclusive withregard to predicting the same or similar effect on the patient 106(assuming that the patient 106 is human in this example). Nonetheless,for example, the clinician 104 may be reminded of a possible side effector other concern that may otherwise have been discounted or forgotten,or, as another example, where the clinician 104 knows that the patient106 has a weakened or dysfunctional pancreas, the above-describedinformation provided by the user interface 132 may be sufficient for theclinician 104 to continue specifying different treatment parameters inthe fields 804-810, in an attempt to determine a more appropriatetreatment for the patient 106.

Similar comments apply regarding an efficacy of specified treatmentparameter(s) with regard to the lungs 108. For example, the firstdataset associated with the human studies may indicate a certain degreeof efficacy of the specified treatment parameters of the fields 804-810(e.g., by way of an appropriate visual indicator, such as color), whilethe second dataset associated with the animal studies may indicate agreater (or lesser) degree of efficacy. In this case, the clinician 104may select the specified treatment parameters for use with the patient106, as compared to alternate treatment parameters. That is, where theclinician 104 is choosing between two or more possible courses oftreatment, the clinician 104 may arrive at a selection of a treatmentbased on a consideration of possible outcomes illustrated by the userinterface 132, based on different ones of the datasets of the field 812.

In addition to diagnosis and treatment of the patient 106, the userinterface 132 may be used, for example, as a research or speculationtool for determining and assessing possible future treatments. Forexample, the clinician 104 may be in the process of determining a futurecourse of research with respect to different (combinations of) treatmentparameters. In deciding between the different courses of research thatmay be taken, the clinician 104 may consider possible outcomes of thetreatment parameters, using the various datasets of the field 812. Forexample, if a particular combination of treatment parameters shows ahigh degree of efficacy (and/or a low degree of unwanted side effects)based on multiple ones of the datasets of the field 812, then theclinician 104 may consider that the particular combination meritsfurther research or clinical-use consideration.

Further in FIG. 8, a field 814 allows the clinician 104 to apply afilter criteria to the dataset(s) specified in the field 812. Forexample, the filter criteria may remove portions of the currentdataset(s) that the clinician 104 may feel have less predictive value indetermining the possible outcome(s) of using the specified treatmentparameters. For example, the clinician may begin consideration ofpossible outcomes of the specified treatment parameters by selecting“aggregation” in the field 812, so that the graphical illustration 802illustrates the possible outcome of use of the treatment parametersbased on all of the datasets of the field 812. Then, the clinician 104may selectively remove a contribution of a selected one or more of thedatasets, by, for example, selecting a dataset associated with “animalstudies” in the field 814, or selecting a filter criteria of “computersimulation(s)” to remove computer-simulated results from the combineddatasets.

In other examples, the filter criteria may not correspond directly or ina one-to-one relationship with one of the datasets of the field 812. Forexample, the filter criteria may filter information from a combinationof datasets, i.e., information that is common to each of the datasets.For example, if the clinician 104 selects “human studies” and “animalstudies” using the field 812, then the clinician 104 may select “after1990” in the field 814 to remove all results from both datasets thatwere collected prior to 1990. Similarly, as shown in FIG. 8, the field814 may be used to filter results from one or more datasets based onwhether the results were obtained in a particular geographical region(i.e., “region x”), or were obtained in studies conducted according to aparticular protocol (i.e., “protocol x”).

Thus, FIG. 8 illustrates an example of a graphical user interfaceincluding at least a first portion (e.g., one or more of the fields804-814) configured to receive a first request to provide a graphicalillustration (e.g., the graphical illustration 802) of a first possibleoutcome of a use of a treatment parameter with respect to at least onebody portion (e.g., the lungs 108 and/or the pancreas 110), based on afirst dataset associated with a first predictive basis (e.g., the firstdataset/first predictive basis selected using the field 812). FIG. 8further illustrates that such a graphical user interface may include atleast a second portion (e.g., one or more of the fields 804-814)configured to receive a second request to provide a modified graphicalillustration (e.g., a modified version of the graphical illustration802) of a second possible outcome of the use of the treatment parameter,based on a second dataset associated with a second predictive basis(e.g., the second dataset/second predictive basis selected using thefield 812). Thus, the graphical user interface also may include a thirdportion configured to illustrate the graphical illustration and themodified graphical illustration (e.g., the portion of the user interface132 of FIG. 8 including the graphical illustration 802), so as, forexample, to include at least a portion of the at least one body portion(e.g., at least a portion of the lungs 108), and/or to include one otherbody portion (e.g., the pancreas 110) in addition to the at least onebody portion (e.g., the lungs 108).

FIG. 9 illustrates an alternative embodiment of the clinical system ofFIG. 1 in which the clinical system is configured to provide access topredictive data. Thus, FIG. 9 illustrates examples by which the userinterface 132 may be used to access or otherwise interact with thetreatment data 126, in order to provide, for example, the variousfeatures, functionalities, and effects described above with respect toFIG. 8.

In the example of FIG. 9, the user interface 132 is illustrated ascontaining generic elements 902 and 904, i.e., a submission element 902and a display element 904. Generally, the submission element 902 mayinclude any icon, button, field, menu, or box that may be used by theclinician 104 to select, submit, or request information. The displayelement 904 may include any element of the user interface 132 used toprovide information to the clinician 104, where it should be understoodthat in some cases the submission element 902 and the display element904 may include the same element, or related elements, since theclinician 104 may enter or select data using a given element and thenmay view the results of the entry or selection using the same element.Thus, and as should be apparent from FIG. 8, the submission element 902may include, for example, any of the fields 804, 806, 808, 812, or 814,since the clinician 104 may submit treatment parameters, datasets,and/or filter criteria therewith. Meanwhile, any of the fields 804-814may be considered to be an example of the display element 904, since anyof these may be used to display information (e.g., a result of aselection of a treatment parameter, dataset, or filter criteria). Ofcourse, the graphical illustration 802 is another example of the displayelement 904.

Thus, for example and as described herein, the clinician 104 may utilizethe submission element(s) 902 to select the treatment parameters (or torequest a suggestion of one or more treatment parameters), or to specifyone or more datasets to be used in providing the possible outcome(s) ofusing the treatment parameters, or to specify a filter criteria to beused in filtering the dataset(s). For example, when the clinician 104uses the field 812 to select the dataset “human studies,” then thissubmission is passed to the treatment logic 128, or, more specifically,is passed to an event handler 906 that receives the submission andperforms an initial classification, logging, routing, or other handlingof the type and value of the submission event, e.g., here, the typeincluding a specification of a dataset to be used and the valueincluding the selected dataset “human studies.”

For example, a submission event associated with a use of the submissionelement 902 by the clinician 104 may be passed by the event handler 906either to dataset view logic 908 and/or filter logic 910. As describedin more detail herein, the dataset view logic 908 and the filter logic910 represent aspects of the treatment logic 128 associated withanalyzing specified treatment parameters with respect to specificportions (e.g., datasets) of the treatment data 126, so as, for example,to provide the uses and effects described above with respect to thegraphical illustration 802, e.g., by using display update logic 912 toupdate the display element(s) 904.

More specifically, for example, the dataset view logic 908 may be usedto analyze a submission event from the event handler 906 and determine,for example, that the clinician 104 has selected both “human studies”and “animal studies” using the field 812. The dataset view logic 908 maythen interact with a query generator 914 of the DBMS engine 130 togenerate a query that may be passed by a database interface 916 to thetreatment data 126. In this case, it also may occur that the eventhandler 906 may pass a second submission event (which may occurconcurrently or in a sequence), in which the clinician 104 selects“before 1990” as a filter criteria in the field 814, to the filter logic910. Thus, the event handler 906 is responsible for correlating the twosubmission events, so that the filter logic 910 may correspond thespecified filter criteria against the (in this case, two) datasetsspecified to the dataset view logic 908.

In these and other examples, then, the treatment logic 128 may interactwith the DBMS engine 130 to construct a query and pass the query to thetreatment data 126. For instance, in the example just given, a query maybe built that includes a Boolean combination of a first datasetassociated with “human studies” AND a second dataset associated with“animal studies,” where the query is generated with a form and structurethat is appropriate for the treatment data 126 (e.g., using theStructured Query Language (SQL) in a case where the treatment data 126implements a relational database).

In FIG. 9, example data results and/or datasets are referenced to FIG.5, where, as shown in FIG. 5, rows 502 and 504 include (abbreviated)data results for a direct end target 306, a target-related, tissueancestry-correlated binding agent 316, and a treatment agent 320. Inthis case, for example, data from the row 502 may be associated with atag 918 indicating that data from the row 502 is associated with humanstudies and should therefore be included in a first dataset, while datafrom the row 504 may be associated with a tag 920 indicating that datafrom the row 504 is associated with animal studies and should thereforebe included in a second dataset (where such examples are intended toillustrate a use of the tags 918, 920 with respect to a query from theDBMS engine 130, and are not intended, necessarily, with specificreference to the Oh reference of FIG. 5). In some implementations, forexample, the tags 918 and 920 may be associated with use of theeXtensible Markup Language (XML) in constructing the treatment data 126,where use of XML or other semi-structured databases is discussed in moredetail, herein. In this case, then, the database interface 916 mayinclude an XML interface.

It should be understood, then, that the tags 918, 920 may be used ingenerating and executing queries against the treatment data 126 byeither the dataset view logic 908 or the filter logic 910. For example,the filter logic 910 may interact with the query generator 914 togenerate a query against the treatment data 126 (or against a result setof a query generated in conjunction with the dataset view logic 908),using the tags 918, 920 to identify, and thereby remove/exclude, datathat matches the filter criteria from a corresponding result set.

Once an appropriate result set(s) has been generated by the dataset viewlogic 908 and/or the filter logic 910, the display update logic 912 maybe used to update the display element 904 appropriately, as referencedherein. For example, the display update logic 912 may include logic forimplementing the color schemes mentioned above, or for providing anyother visual indicator(s) that may be used to convey information inassociation with the graphical illustration 802.

FIG. 10 illustrates an operational flow representing example operationsrelated to accessing predictive data. In FIG. 10 and in followingfigures that include various examples of operational flows, discussionand explanation may be provided with respect to the above-describedexamples of FIGS. 1-9, and/or with respect to other examples andcontexts. However, it should be understood that the operational flowsmay be executed in a number of other environments and contexts, and/orin modified versions of FIGS. 1-9. Also, although the variousoperational flows are presented in the sequence(s) illustrated, itshould be understood that the various operations may be performed inother orders than those which are illustrated, or may be performedconcurrently.

After a start operation, the operational flow 1000 moves to a providingoperation 1010 where a graphical illustration of a first possibleoutcome of a use of a treatment parameter with respect to at least onebody portion is provided, based on a first dataset associated with afirst predictive basis. For example, as shown in FIG. 8, the graphicalillustration 802 may be provided using the user interface 132, where thegraphical illustration 802 may provide a first possible outcome of a useof one or more of the treatment parameters of the fields 804-810 of FIG.8. As described herein, the first possible outcome may be based on afirst dataset that may be specified, for example, using the field 812,where the first predictive basis corresponds to the first dataset andmay be pre-configured, defined, or characterized (e.g., as being eitherrelatively more or less predictively useful than a comparison dataset).Although the graphical illustration 802 includes specific body portionssuch as the lungs 108 and pancreas 110, it should be understood that thegraphical illustration 802 may be provided with respect to an entirebody of the patient 106 (e.g., where the treatment parameter includes ablood pressure or other characteristic of the patient 106 that is notlocalized to a particular body portion), and may be provided as anadditional or alternative representation of data (e.g., as a bloodpressure chart illustrated along with, or as some or all of, thegraphical illustration 802).

Then, in a modifying operation 1020, the graphical illustration may bemodified to illustrate a second possible outcome of the use of thetreatment parameter, based on a second dataset associated with a secondpredictive basis. For example, the graphical illustration 802 of FIG. 8may be modified in response to a subsequent selection by the clinician104 of the second dataset (e.g., by selecting “animal studies” in thefield 812). In this sense, it should be understood that the modifyingmay occur by altering the graphical illustration 802 or some partthereof, including new or additional aspects of the graphicalillustration 802, or replacing some or all of the graphical illustration802, e.g., in conjunction with any of the techniques described abovewith respect to the providing operation 1010, or with other techniques,including the use of visual indicators and/or illustrated/highlightedbody portions.

In this regard, it should be understood that the providing operation1010 and/or the modifying operation 1020 may be performed with respectto a digital representation (e.g., as digital data), for example, of thetreatment parameter, the dataset(s), and/or the filter criteria. Forexample, as may be understood with reference to FIG. 9, the treatmentlogic 128 may accept a digital or analog (for conversion into digital)representation of the at least one treatment parameter from the userinterface 132 (e.g., from the submission element 902), for presentationto the DBMS engine 130 and/or the treatment data 126. As anotherexample, the treatment logic 128 may provide a digitally-encodedrepresentation of the graphical illustration 802, or a modified versionthereof, based on the treatment data 126, where the treatment data 126may be implemented and accessed locally, and/or may be implemented andaccessed remotely.

Thus, an operation(s) may be performed related either to a local orremote storage of the digital data, or to another type of transmissionof the digital data. As discussed herein, in addition to accessing,querying, recalling, or otherwise obtaining the digital data for theproviding operation, operations may be performed related to storing,assigning, associating, or otherwise archiving the digital data to amemory, including, for example, sending and/or receiving a transmissionof the digital data from a remote memory. Accordingly, any suchoperation(s) may involve elements including at least an operator (e.g.,either human or computer) directing the operation, a transmittingcomputer, and/or a receiving computer, and should be understood to occurwithin the United States as long as at least one of these elementsresides in the United States.

FIG. 11 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 11 illustrates example embodiments where theproviding operation 1010 may include at least one additional operation.Additional operations may include operation 1102, operation 1104,operation 1106, operation 1108, operation 1110, and/or operation 1112.

At the operation 1102, a request for the graphical illustration isreceived by way of a graphical user interface. For example, a requestfrom the clinician 104 may be received for the graphical illustration802, using the user interface 132. Then, at the operation 1104, thegraphical illustration may be provided in response to the request. Forexample, and continuing the example just given, the graphicalillustration 802 may be provided in response to the request of theclinician 104.

At an operation 1106, the graphical illustration including anillustration of at least a part of a body having the at least one bodyportion included therein may be provided, the at least one body portionbeing visually altered to represent the first possible outcome. Forexample, as shown in FIG. 8, the graphical illustration 802 may beprovided with the at least a part of a body having the at least one bodyportion (e.g., the lungs 108) included therein. Further, and asreferenced herein, the at least one body portion (e.g., the lungs 108)may be visually altered with respect to a remainder of the graphicalillustration 802, e.g., the lungs 108 may be highlighted, identified,altered in color or intensity or appearance, caused to flash at acertain repetition-frequency, or otherwise be visually marked fornotification to the clinician 104.

At an operation 1108, the graphical illustration may be providedincluding at least a portion of one or more of an organ, an organsystem, an organ subsystem, diseased tissue, and/or healthy tissue asthe at least one body portion. For example, as illustrated in FIG. 8,the at least one body portion may include the lungs 108 and/or thepancreas 110.

At an operation 1110, the graphical illustration may be providedincluding the at least one body portion in association with a visualindicator related to the first possible outcome. For example, thegraphical illustration 802 of FIG. 8 may be provided including the lungs108, pancreas 110, or other body portion, where the mere inclusion ofsuch a body portion may be considered to be a visual indicator relatedto the first possible outcome (e.g., where the pancreas 110 isillustrated only when it is determined by the treatment logic 128 thatpossible side effects may be associated with the pancreas 110 when usingthe lungs 108 (or cancerous cells therein) as a direct end target). Inother implementations, and as referenced herein, the visual indicatormay include a coloring, highlighting, designating, marking, identifying,shading, cross-hatching, flashing, or other visual effect. In suchexamples, the visual indicator(s) may be related to, or indicate, thefirst possible outcome, e.g., the efficacy (or risks, or unwantedconsequences) of one or more (combinations of) treatment parameters. Forexample, the graphical illustration 802 or appropriate portion(s)thereof may have its color changed, or may be highlighted or otherwisemarked/designated to indicate a level of efficacy of selected treatmentparameter(s). For example, an efficacy of each treatment parameter maybe shown individually or together, since, for example, an efficacy ofthe target-related, tissue ancestry-correlated binding agent of thefield 808 may refer to an ability of such an agent to deliver anytreatment agent to (a corresponding target-related, tissueancestry-correlated binding site within) a direct end target of thefield 804, irrespective of which treatment agent is associatedtherewith. Meanwhile, an efficacy of the treatment agent of the field806 may refer to an actual treatment result (e.g., reduction ordestruction of cancer cells), and, in another example, an efficacy ofthe combination of treatment parameters may refer to an overall successof the treatment, including management or reduction of associated risksand side effects.

At the operation 1112, the graphical illustration of the first possibleoutcome is provided including a representation of a systemic consequenceof the use of the treatment parameter. For example, as referencedherein, the first possible outcome may include raised/lowered bloodpressure, raised/lowered temperature, or other outcomes that aresystemic to the patient 106, and which may or may not be displayed inthe context of an illustration of a specific organ or otherhighly-localized body portion. For example, as described with referenceto the row 706 of FIG. 7, application of the target-related tissueancestry correlated binding agent of the field 808 may occur withrespect to a number of pre-metastatic niches, which may include verysmall sites within (or in association with) one or more tissues ororgans (e.g., the lungs 108). In such cases, the graphical illustration802 may include a representation of such systemic consequences, e.g., byvisual indicators that are distributed in an appropriate and/orrepresentative fashion within an illustration of a body of the patient106, or by an ancillary chart, graph, or other representation that maybe used to present or describe at least some aspect of the firstpossible outcome.

FIG. 12 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 12 illustrates example embodiments where theproviding operation 1010 may include at least one additional operation.Additional operations may include operation 1202, operation 1204,operation 1206, operation 1208, and/or operation 1210.

At the operation 1202, the graphical illustration may be provided inresponse to a request that characterizes the first predictive basis asincluding at least one actual and/or theoretical analysis of the use ofthe treatment parameter. For example, the user interface 132 may receivea request from the clinician 104 through a selection of one of thevalues of the field 812, where the field 812 may include an actualanalysis (e.g., human studies or animal studies, or any in vivo or invitro study) and/or a theoretical analysis (e.g., in silico and/orcomputer simulations, or speculation).

At the operation 1204, the graphical illustration may be provided inresponse to a request that characterizes the first predictive basis asincluding one or more of a human study, an animal study, a computersimulation, a speculation, and/or a professionally-informed speculation.For example, the user interface 132 may receive the request from theclinician 104 by way of the field 812, where the clinician 104 may usethe field 812 to specify human studies, animal studies, or any of theother predictive bases included therein, or other predictive bases thatmay be provided, or combinations thereof (where it will be appreciatedthat a human study or animal study (or similar terminology), includes ahuman-based study or an animal-based study, respectively).

At the operation 1206, the graphical illustration of (e.g., associatedwith) the first possible outcome may be provided including a possibleside effect of the use of the treatment parameter. For example, thegraphical illustration 802 may include the pancreas 110, where thepancreas 110 may be affected by the desired treatment for treating thecancer cells in the lungs 108. Similarly, the graphical illustration 802may include a visual indicator associated with the pancreas 110, wherean appearance of the visual indicator 110 (e.g., brightness, colorshade, or frequency of flashing) may be indicative of a type or extentof the side effect. In another example, a chart, text box, or othercall-out may be included in conjunction with the human body image of thegraphical illustration 802, in which the nature or extent of the sideeffect may be included for review by the clinician 104.

At the operation 1208, the graphical illustration of the first possibleoutcome may be provided including a possible risk (e.g., a traditionalrisk and/or ambiguity) of the use of the treatment parameter. Forexample, the graphical illustration 802 may be provided withcorresponding visual indicators, call-outs, or other informationdesigned to inform the clinician 104 of risks associated with one ormore of the treatment parameters specified in the fields 804-810. Forexample, such risks or unwanted impacts may include a risk of loweredefficacy of one or more treatment parameters where certain treatmentparameters are used in combination, e.g., where the clinician 104selects the combination of treatment parameters to avoid certain sideeffects, at the possible cost of a lowered efficacy of the treatment asa whole.

At the operation 1210, the graphical illustration may be providedincluding a representation of the first possible outcome with respect toa possible efficacy of the use of the treatment parameter. For example,the user interface 132 may provide the graphical illustration 802 asincluding a visual illustration of the possible outcome, perhapsincluding an illustration of a healthy organ (e.g., the lungs 108) toillustrate success of a specified treatment.

FIG. 13 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 13 illustrates example embodiments where theproviding operation 1010 may include at least one additional operation.Additional operations may include operation 1302, operation 1304, and/oroperation 1306.

At the operation 1302, the graphical illustration of the first possibleoutcome may be provided based on the first dataset, the first datasetbeing associated with both the first predictive basis and at least oneother predictive basis. For example, the graphical illustration 802 maybe provided based on a first dataset specified in the field 812 as beingassociated with a first predictive basis, such as “human studies,” whereresults from at least one other dataset may be included in the firstdataset having another predictive basis, such as “animal studies.” Inother words, results from different studies, datasets, and/or predictivebases may be combined to provide the graphical illustration 802, sothat, as described herein, the clinician 104 may consider suchcombinations when deciding on a diagnosis, treatment, or course ofresearch. In this regard, and as described in more detail herein, itshould be understood that in many cases, a predictive basis of “humanstudies” may be assumed to be more predictively useful than a predictivebasis of “animal studies” in the context of deciding diagnosis,treatment, or research for human patients. More generally, however, apredictive basis and/or a relative predictive value thereof may beassigned or associated with results, data, or datasets within thetreatment data 126, prior to a use of the user interface 132 by theclinician 104, using, e.g., the tags 918 and 920, or similar techniques.That is, in some implementations, different predictive bases may beobjectively and verifiably designated as having a defined relative valueof predictive usefulness (e.g., relative to one another). Accordingly,one skilled in the art would appreciate that no subjectivity is involvedin providing the graphical illustration 802 (as described herein) basedon the different predictive bases, as those predictive bases may beprovided in associated software, hardware, and/or firmware. Of course,the graphical illustration 802 may nonetheless have more or lesssubjective value to the clinician 104, based on a personal value orjudgment of the clinician 104. In other implementations, an artificialintelligence engine may be used to make semantic decisions regardingassessment(s) of the relative predictive value or usefulness of thedifferent predictive bases.

At the operation 1304, the graphical illustration may be provided basedon the first dataset, the first dataset being associated with the firstpredictive basis through a characterization of the first dataset, thefirst predictive basis, and/or a relationship between the first datasetand the first predictive basis. For example, as just referenced, thegraphical illustration 802 may be provided based on a first dataset thatincludes “human studies” as specified in the field 812. More generally,different datasets including different human studies, or different typesof human studies, may be included, where each such dataset may becharacterized as having a certain predictive value with regard to thepossible outcome. For example, such a characterization may beimplemented through use of the tags 918, 920, or using some othertechnique for characterizing data within the treatment data 126. Suchcharacterizations may be universal through the treatment data 126, sothat, for example, all human studies of a certain type are associatedwith a first predictive basis or value. In other implementations, suchcharacterizations may be assigned by, or determined for, individualclinicians. For example, different clinicians may assign differentpredictive values to different (types of) datasets.

At the operation 1306, the graphical illustration may be provided basedon the first dataset, the first dataset being associated with the firstpredictive basis through a characterization of the first dataset withrespect to one or more of a source of the first dataset, a funding ofthe first dataset, a research field of the first dataset, a time periodof collection of the first dataset, or a location of collection of thefirst dataset. For example, the first dataset may be characterized by asource of funding of the research that supplied the results of the firstdataset, where, for example, a certain funding source may be associatedwith a higher predictive value than others. Similarly, a research fieldassociated with the first dataset (e.g., oncology or hematology) may beassociated with, or characterized as, having a greater or lesserpredictive value, e.g., through use of the tags 918, 920, or using otherdata characterization techniques.

Alternatively, for the operation 1306 (not shown), the graphicalillustration may be provided based on the first dataset, the firstdataset being associated with the first predictive basis through acharacterization of the first dataset with respect to one or more of asource of the first dataset, a procedural aspect of the first dataset, asource of support associated with the first dataset, a research field ofthe first dataset, a time period or time interval of collection of thefirst dataset, a professional publication associated with the firstdataset, a professional author or investigator associated with the firstdataset, or a location of collection of the first dataset. For example,the first dataset may be characterized by a source or nature of thesupport of the research that supplied the results of the first dataset,where, for example, a certain nature of support may be associated with ahigher net predictive value than others. Similarly, a research fieldassociated with the first dataset (e.g., oncology or hematology) may beassociated with, or characterized as, having a greater or lesserpredictive value, e.g., through use of the tags 918, 920, or using otherdata characterization techniques.

FIG. 14 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 14 illustrates example embodiments where theproviding operation 1010 may include at least one additional operation.Additional operations may include operation 1402, operation 1404,operation 1406, operation 1408, operation 1410, operation 1412, and/oroperation 1414.

At the operation 1402, the graphical illustration of the first possibleoutcome of the use of the treatment parameter may be provided, where thetreatment parameter may include one or more of at least onetarget-related tissue ancestry-correlated binding site; at least onetarget-related tissue ancestry-correlated binding agent, at least onedirect end target, at least one discriminated end target, at least onedirect intermediate target, at least one discriminated intermediatetarget, at least one treatment agent delivery mechanism relative to theat least one target-related tissue ancestry-correlated binding agent, atleast one treatment agent, or at least one treatment agent precursor.For example, the graphical illustration 802 may illustrate a firstpossible outcome of the use of one or more of a direct end target, atreatment agent, or a target-related tissue ancestry-correlated bindingagent, as these or other examples of the operation 1402 may be selected,provided, or otherwise specified, using the fields 804-810, or similarfields.

At the operation 1404, a request for the graphical illustration may bereceived, the request specifying at least one target-related tissueancestry-correlated binding site associated with the at least one bodyportion as the treatment parameter. For example, the user interface 132may receive a request from the clinician 104 through the use of a fieldsimilar to the fields 804-810, or through another display element 902,wherein the clinician 104 specifies, for example, at least one proteininduced and/or expressed at an interface (e.g., the endothelial layer118) between tissue and/or blood and/or a blood component in thevicinity of the at least one body portion as the at least onetarget-related tissue ancestry-correlated binding site. Then, at theoperation 1406, the graphical illustration of the first possible outcomeof the use of the at least one target-related tissue ancestry-correlatedbinding site may be provided, the graphical illustration illustrating atleast a portion of the at least one body portion. For example, the atleast a portion of the at least one body portion (e.g., at least aportion of the lungs 108) may be illustrated in the graphicalillustration 802 (e.g., including a visual indicator) as to an efficacyof the at least one target-related tissue ancestry-correlated bindingsite in serving as a molecular address for the treatment agent of thefield 806.

At the operation 1408, a request for the graphical illustration may bereceived, the request specifying at least one target-related tissueancestry-correlated binding agent associated with the at least one bodyportion as the treatment parameter. For example, the user interface 132may receive a request for the at least one target-related tissueancestry-correlated binding agent by way of the field(s) 808 and/or 810.The at least one target-related tissue ancestry-correlated binding agentmay include, for example, an I-labeled monoclonal antibody that is knownto target and bind to a corresponding target-related tissueancestry-correlated binding site. Then, at the operation 1410, thegraphical illustration of the first possible outcome of the use of theat least one target-related tissue ancestry-correlated binding agent maybe provided, the graphical illustration illustrating at least a portionof the at least one body portion. For example, as in FIG. 8, if thetarget-related tissue ancestry-correlated binding agent is associatedwith a corresponding target-related tissue ancestry-correlated bindingsite of the lungs 108 (or certain cancerous cells thereof), then thelungs 108 may be illustrated in whole or in part.

At the operation 1412, a request for the graphical illustration may bereceived, the request specifying at least one direct end targetassociated with the at least one body portion as the treatmentparameter. For example, the request for the graphical illustration 802to include the (requested) direct end target may be specified using thefield 804 of the user interface 132. Then, at the operation 1414, thegraphical illustration of the first possible outcome of the use of theat least one direct end target may be provided, the graphicalillustration illustrating at least a portion of the at least one bodyportion. For example, the graphical illustration 802 may be provided asshowing the lungs 108 or the pancreas 110 as the at least one bodyportion (since, in the latter case, the lungs 108 are associated withthe pancreas 110 by virtue of an (undesired) effect on the pancreas 110that may be included in the first possible outcome).

FIG. 15 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 15 illustrates example embodiments where theproviding operation 1010 may include at least one additional operation.Additional operations may include operation 1502, operation 1504,operation 1506, operation 1508, operation 1510, operation 1512,operation 1514, operation 1516, operation 1518, and/or operation 1520.

At the operation 1502, a request for the graphical illustration isreceived, the request specifying at least one discriminated end targetassociated with the at least one body portion as the treatmentparameter. For example, although not illustrated in FIG. 8, acorresponding field of the user interface 132 may be used to receive therequest for the at least one discriminated end target. Then, at theoperation 1504, the graphical illustration of the first possible outcomeof the use of the at least one discriminated end target may be provided,the graphical illustration illustrating at least a portion of the atleast one body portion. For example, the discriminated end target mayinclude non-lung tissue/organ(s) (e.g., the pancreas 110), and/ornon-cancerous lung tissue, so that one or more of these may be includedin the graphical illustration 802.

At the operation 1506, a request for the graphical illustration may bereceived, the request specifying at least one direct intermediate targetassociated with the at least one body portion as the treatmentparameter. For example, the direct intermediate target may be specifiedusing a field (not shown) of FIG. 8. Then, at the operation 1508, thegraphical illustration of the first possible outcome of the use of theat least one direct intermediate target may be provided, the graphicalillustration illustrating at least a portion of the at least one bodyportion. For example, the direct intermediate target may includeendothelial tissue proximate to (e.g., cancerous) lung tissue, so thatthe graphical illustration 802 may illustrate at least a portion of thelungs 108.

At the operation 1510, a request for the graphical illustration may bereceived, the request specifying at least one discriminated intermediatetarget associated with the at least one body portion as the treatmentparameter. For example, the discriminated intermediate target may bespecified using a field (not shown) of FIG. 8. Then, at the operation1512, the graphical illustration of the first possible outcome of theuse of the at least one discriminated intermediate target may beprovided, the graphical illustration illustrating at least a portion ofthe at least one body portion. For example, the discriminatedintermediate target may include endothelial tissue proximate to non-lungtissue (e.g., endothelial tissue proximate to the pancreas 110), so thatthe graphical illustration 802 may illustrate the lungs 108 and/or thepancreas 110 (or a portion thereof) as the at least a portion of the atleast one body portion.

At the operation 1514, a request for the graphical illustration may bereceived, the request specifying at least one treatment agent associatedwith the at least one body portion as the treatment parameter. Forexample, the user interface 132 may receive the request by way of thefield 806. Then, at the operation 1516, the graphical illustration ofthe first possible outcome of the use of the at least one treatmentagent may be provided, the graphical illustration illustrating at leasta portion of the at least one body portion. For example, where thetreatment agent includes radionuclides that are associated with cancercells in the lung through a desired course of treatment, then thegraphical illustration 802 may provide an illustration of the lungs 108,perhaps with a visual indicator to indicate a presence of the treatmentagent (e.g., radionuclides).

At the operation 1518, a request for the graphical illustration may bereceived, the request specifying at least one treatment agent precursorassociated with the at least one body portion as the treatmentparameter. For example, the treatment agent precursor may be specifiedusing a field (not shown) of FIG. 8. Then, at the operation 1520, thegraphical illustration of the first possible outcome of the use of theat least one treatment agent precursor may be provided, the graphicalillustration illustrating at least a portion of the at least one bodyportion. For example, the treatment agent precursor may include an agentused to facilitate application of a treatment agent, e.g., animmune-response element that is used to identify/mark/bond with atarget-related tissue ancestry-correlated binding site and/or asubstance that when metabolized becomes the treatment agent, such aswith pro-drugs.

FIG. 16 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 16 illustrates example embodiments where themodifying operation 1020 may include at least one additional operation.Additional operations may include operation 1602, operation 1604,operation 1606, operation 1608, and/or operation 1610.

At the operation 1602, a visual indicator associated with the at leastone body portion may be modified within the graphical illustration. Forexample, where the first predictive basis supporting the first possibleoutcome includes “human studies” selected for the field 812, the secondpredictive basis supporting the second possible outcome may include“animal studies,” or a combination of “human studies” and “animalstudies,” as described herein. Accordingly, the second possible outcomemay be different than the first possible outcome in some respect, due,for example, to the differences in the predictive bases. For example,the first dataset/first predictive basis may result in the firstpossible outcome showing a high efficacy of the treatment agent in thefield 806, and/or a relatively low incidence of impact of the treatmentagent on undesired tissue (e.g., on the pancreas 110). Meanwhile, thesecond dataset/second predictive basis may result in the second possibleoutcome showing, for example, an even higher efficacy of the treatmentagent, combined with a relatively high degree of impact of the treatmentagent on undesired tissue. Thus, in the operation 1602, a visualindicator associated with the lungs 108, such as, for example, a color,may be modified to indicate/illustrate such changes between the firstpossible outcome and the second possible outcome.

At the operation 1604, a visual indicator associated with at least oneother body portion within the graphical illustration may be modified.For example, and continuing the example(s) just provided, a color orother visual indicator associated with at least one other body portionbesides the lungs 108, e.g., the pancreas 110, may be modified.

At the operation 1606, the graphical illustration may be modified basedon the first dataset and the second dataset to incorporate the firstpredictive basis and the second predictive basis in the second possibleoutcome. For example, as described herein, the first dataset (e.g., adataset associated with “human studies” in the field 812) may becombined with the second dataset (e.g., a dataset associated with“animal studies” in the field 812), in order to provide a cumulative orcombined predictive basis as the second predictive basis.

At the operation 1608, the graphical illustration may be modified basedon the second dataset to replace the first predictive basis with thesecond predictive basis in providing (e.g., associated with) the secondpossible outcome. For example, and similarly to the example just given,rather than combine (results from) the datasets “human studies” and“animal studies” to obtain the second predictive basis, a replacementmay occur in which the dataset associated with “animal studies” replacesthe dataset associated with “human studies” in providing/modifying thegraphical illustration 802.

At the operation 1610, the graphical illustration may be modified basedon the second dataset to reflect a weighting of first predictive basiswith respect to the second predictive basis in providing (e.g.,associated with) the second possible outcome. For example, considering acase where the first dataset includes datasets for “human studies” and“animal studies,” the second dataset may include a new or modifiedweighting of the two predictive bases relative to one another within thesecond dataset, e.g., to increase a relative importance of one relativeto the other in providing the second predictive basis for the secondpossible outcome.

FIG. 17 illustrates alternative embodiments of the example operationalflow 1000 of FIG. 10. FIG. 17 illustrates example embodiments where themodifying operation 1020 may include at least one additional operation.Additional operations may include operation 1702, operation 1704,operation 1706, operation 1708, and/or operation 1710.

At the operation 1702, the graphical illustration may be modified basedon the second dataset associated with the second predictive basis, thesecond predictive basis being relatively less predictively useful thanthe first predictive basis. For example, a data manager/creator of thetreatment data 126, which may include the clinician 104, may make suchassignment or association between different predictive bases relative toone another. As described herein, for example, the second datasetassociated with “animal studies” may be deemed to be less predictivelyuseful than the first dataset associated with “human studies.” In otherexamples, different types of “human studies” may beincluded/specified/requested, and may be pre-designated, e.g., using thetags 918, 920, to indicate an assigned level of predictive usefulness,based, for example, on a time, location, or protocol of the studies, oron a preference of the clinician 104, or on some other criteria. In thisway, a burden on, or effort of, the clinician 104 may be reduced, whilethe clinician 104 is allowed, for example, to expand a query as to anefficacy of the treatment parameter by considering datasets beingcharacterized as being relatively less (but still meaningfully)predictively useful. For example, the graphical illustration 802 may beprovided/modified to reflect the second possible outcome and the secondpredictive basis, with a color (or other visual indicator) of therelevant body portion(s) being altered accordingly.

At the operation 1704, the graphical illustration based on the seconddataset associated with the second predictive basis, the secondpredictive basis being relatively more predictively useful than thefirst predictive basis. For example, and in contrast to the example justgiven with respect to the operation 1702, the second dataset may includeresults from “human studies,” which may be deemed to be morepredictively useful, e.g., in the sense just described.

At the operation 1706, the graphical illustration may be modified toillustrate the second possible outcome with respect to a potentialefficacy of the use of the treatment parameter relative to the firstpossible outcome. For example, the graphical illustration 802 may bemodified in visual appearance in any of the manners described herein, orin other manners not necessarily described, in order to illustrate thesecond possible outcome with respect to a potential efficacy of the useof the treatment parameter relative to the first possible outcome. Forexample, the lungs 108 may be visually modified within the graphicalillustration 802, relative to a visual appearance of the lungs 108 inassociation with the providing operation 1010.

At the operation 1708, the graphical illustration may be modified toillustrate the second possible outcome with respect to a potential risk(e.g., a traditional risk or an ambiguity) of the use of the treatmentparameter relative to the first possible outcome. For example, thegraphical illustration 802 may be modified in visual appearance in anyof the manners described herein, or in other manners not necessarilydescribed, in order to illustrate the second possible outcome withrespect to a potential risk of the use of the treatment parameterrelative to the first possible outcome.

At the operation 1710, the graphical illustration may be modified toillustrate a possible side effect of the use of the treatment parameter.For example, the graphical illustration 802 may be modified toillustrate the second possible outcome with respect to a side effect ofthe use of the treatment parameter relative to the first possibleoutcome. For example, a side effect of the treatment agent of the field806 on the pancreas 110 may be included in the second possible outcomebased on the second dataset/second predictive basis, by altering avisual appearance of or indicator associated with, the pancreas 110.

FIG. 18 illustrates a partial view of an example computer programproduct 1800 that includes a computer program 1804 for executing acomputer process on a computing device. An embodiment of the examplecomputer program product 1800 is provided using a signal bearing medium1802, and may include at least one of one or more instructions forproviding a graphical illustration of a first possible outcome of a useof a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basis, andone or more instructions for modifying the graphical illustration toillustrate a second possible outcome of the use of the treatmentparameter, based on a second dataset associated with a second predictivebasis. The one or more instructions may be, for example, computerexecutable and/or logic-implemented instructions. In one implementation,the signal-bearing medium 1802 may include a computer-readable medium1806. In one implementation, the signal bearing medium 1802 may includea recordable medium 1808. In one implementation, the signal bearingmedium 1802 may include a communications medium 1810.

FIG. 19 illustrates an example system 1900 in which embodiments may beimplemented. The system 1900 includes a computing system environment.The system 1900 also illustrates the clinician 104 using a device 1904,which is optionally shown as being in communication with a computingdevice 1902 by way of an optional coupling 1906. The optional coupling1906 may represent a local, wide-area, or peer-to-peer network, or mayrepresent a bus that is internal to a computing device (e.g., in exampleembodiments in which the computing device 1902 is contained in whole orin part within the device 1904). A storage medium 1908 may be anycomputer storage media.

The computing device 1902 includes computer-executable instructions 1910that when executed on the computing device 1902 cause the computingdevice 1902 to provide a graphical illustration of a first possibleoutcome of a use of a treatment parameter with respect to at least onebody portion, based on a first dataset associated with a firstpredictive basis, and to modify the graphical illustration to illustratea second possible outcome of the use of the treatment parameter, basedon a second dataset associated with a second predictive basis. Asreferenced above and as shown in FIG. 19, in some examples, thecomputing device 1902 may optionally be contained in whole or in partwithin the clinician device 1904.

In FIG. 19, then, the system 1900 includes at least one computing device(e.g., 1902 and/or 1904). The computer-executable instructions 1910 maybe executed on one or more of the at least one computing device. Forexample, the computing device 1902 may implement the computer-executableinstructions 1910 and output a result to (and/or receive data from) thecomputing (clinician) device 1904. Since the computing device 1902 maybe wholly or partially contained within the computing (clinician) device1904, the computing (clinician) device 1904 also may be said to executesome or all of the computer-executable instructions 1910, in order to becaused to perform or implement, for example, various ones of thetechniques described herein, or other techniques.

The clinician device 1904 may include, for example, one or more of apersonal digital assistant (FDA), a laptop computer, a tablet personalcomputer, a networked computer, a computing system comprised of acluster of processors, a workstation computer, and/or a desktopcomputer. In another example embodiment, the computing device 1902 maybe operable to communicate with the clinician device 1904 associatedwith the clinician 104 to receive information regarding theidentification and to provide the at least two instances of the at leastone treatment characteristic from at least one memory.

In addition to references described above, the following are also herebyincorporated by reference in their entireties to the extent such are notinconsistent herewith:

-   Pasqualini et al., “Probing the Structural and Molecular Diversity    of Tumor Vasculature,” TRENDS in Molecular Medicine, vol. 8, No. 12,    pp. 563-571 (December 2002);-   Aird et al., “Vascular Bed-specific Expression of an Endothelial    Cell Gene is Programmed by the Tissue Microenvironment,” The Journal    of Cell Biology, vol. 138, No. 5, pp. 1117-1124 (Sep. 8, 1997);-   Pasqualini et al., “Organ Targeting In Vivo Using Phage Display    Peptide Libraries,” Nature, vol. 380, pp. 364-366 (Mar. 28, 1996);-   Rajotte et al., “Molecular Heterogeneity of the Vascular Endothelium    Revealed by In Vivo Phage Display,” J. Clin. Invest., vol. 102, No.    2, pp. 430-437 (July 1998);-   M′Rini, et al., “A Novel Endothelial L-Selectin Ligand Activity in    Lymph Node Medulla That Is Regulated by    (1,3)-Fucosyltransferase-IV,” J. Exp. Med., vol. 198, No. 9, pp.    1301-1312 (Nov. 3, 2003);-   Carver, et al., “Caveolae: Mining Little Caves for New Cancer    Targets,” Nature Reviews, vol. 3, pp. 571-572 (August 2003);-   Folkman, Judah, “Looking For A Good Endothelial Address,” Cancer    Cell, pp. 113-115 (March 2002);-   Brody, Lawrence C., “Treating Cancer by Targeting a Weakness,” N    Engl J Med, 353; 9 pp. 949-950 (1 Sep. 2005);-   Farmer, et al., “Targeting the DNA Repair Defect in BRCA Mutant    Cells as a Therapeutic Strategy,” Nature, vol. 434, pp. 917-921 (14    Apr. 2005);-   Bryant, et al., “Specific Killing of BRCA2-Deficient Tumours with    Inhibitors of poly(ADP-ribose) Polymerase,” Nature, vol. 434, pp.    913-917 (14 Apr. 2005);-   Hsu, et al., “Neural Systems Responding to Degrees of Uncertainty in    Human Decision-Making,” Science, vol. 310, pp. 1680-1683 (9 Dec.    2005);-   Kaplan, et al., “VEGFR1-Positive Haematopoietic Bone Marrow    Progenitors Initiate The Pre-Metastatic Niche,” Nature, vol. 438,    pp. 820-825 (8 Dec. 2005).

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermediate components. Likewise, any two componentsso associated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.Any two components capable of being so associated can also be viewed asbeing “operably couplable” to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While certain features of the described implementations have beenillustrated as disclosed herein, many modifications, substitutions,changes and equivalents will now occur to those skilled in the art. Itis, therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the embodiments of the invention.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from this subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of this subject matter describedherein. Furthermore, it is to be understood that the invention is solelydefined by the appended claims. It will be understood by those withinthe art that, in general, terms used herein, and especially in theappended claims (e.g., bodies of the appended claims) are generallyintended as “open” terms (e.g., the term “including” should beinterpreted as “including but not limited to,” the term “having” shouldbe interpreted as “having at least,” the term “includes” should beinterpreted as “includes but is not limited to,” etc.). It will befurther understood by those within the art that if a specific number ofan introduced claim recitation is intended, such an intent will beexplicitly recited in the claim, and in the absence of such recitationno such intent is present. For example, as an aid to understanding, thefollowing appended claims may contain usage of the introductory phrases“at least one” and “one or more” to introduce claim recitations.However, the use of such phrases should not be construed to imply thatthe introduction of a claim recitation by the indefinite articles “a” or“an” limits any particular claim containing such introduced claimrecitation to inventions containing only one such recitation, even whenthe same claim includes the introductory phrases “one or more” or “atleast one” and indefinite articles such as “a” or “an” (e.g., “a” and/or“an” should typically be interpreted to mean “at least one” or “one ormore”); the same holds true for the use of definite articles used tointroduce claim recitations. In addition, even if a specific number ofan introduced claim recitation is explicitly recited, those skilled inthe art will recognize that such recitation should typically beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, typicallymeans at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, and C”would include but not be limited to systems that have A alone, B alone,C alone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). In those instances where a conventionanalogous to “at least one of A, B, or C, etc.” is used, in general sucha construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that any disjunctive word and/orphrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

What is claimed is:
 1. An article of manufacture including at least anon-transitory signal bearing medium bearing at least: (a) one or moreinstructions relating to providing a graphical illustration of a firstpossible outcome of a use of a treatment parameter with respect to atleast one body portion, based on a first dataset associated with a firstpredictive basis, the first dataset being associated with the firstpredictive basis through a characterization of the first dataset, thefirst predictive basis, and/or a relationship between the first datasetand the first predictive basis, and including at least one of: (i) oneor more instructions related to providing the graphical illustration ofthe first possible outcome including a possible side effect of the useof the treatment parameter; or (ii) one or more instructions related toproviding the graphical illustration including a representation of thefirst possible outcome with respect to a possible efficacy of the use ofthe treatment parameter; and (b) one or more instructions relating tomodifying the graphical illustration to illustrate a second possibleoutcome of the use of the treatment parameter, based on a second datasetassociated with a second predictive basis.
 2. The article of manufactureof claim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to receiving a request for the graphicalillustration by way of a graphical user interface; and one or moreinstructions related to providing the graphical illustration in responseto the request.
 3. The article of manufacture of claim 1 wherein the oneor more instructions related to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis comprises: one or more instructions related toproviding the graphical illustration including an illustration of atleast a part of a body having the at least one body portion includedtherein, the at least one body portion being visually altered torepresent the first possible outcome.
 4. The article of manufacture ofclaim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to providing the graphical illustrationincluding at least a portion of one or more of an organ, an organsystem, an organ subsystem, diseased tissue, and/or healthy tissue asthe at least one body portion.
 5. The article of manufacture of claim 1wherein the one or more instructions related to providing a graphicalillustration of a first possible outcome of a use of a treatmentparameter with respect to at least one body portion, based on a firstdataset associated with a first predictive basis comprises: one or moreinstructions related to providing the graphical illustration includingthe at least one body portion in association with a visual indicatorrelated to the first possible outcome.
 6. The article of manufacture ofclaim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to providing the graphical illustration ofthe first possible outcome including a representation of a systemicconsequence of the use of the treatment parameter.
 7. The article ofmanufacture of claim 1 wherein the one or more instructions related toproviding a graphical illustration of a first possible outcome of a useof a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basiscomprises: one or more instructions related to providing the graphicalillustration in response to a request that characterizes the firstpredictive basis as including at least one actual and/or theoreticalanalysis of the use of the treatment parameter.
 8. The article ofmanufacture of claim 1 wherein the one or more instructions related toproviding a graphical illustration of a first possible outcome of a useof a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basiscomprises: one or more instructions related to providing the graphicalillustration in response to a request that characterizes the firstpredictive basis as including one or more of a human study, an animalstudy, a computer simulation, a speculation, and/or aprofessionally-informed speculation.
 9. The article of manufacture ofclaim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to providing the graphical illustration ofthe first possible outcome including a possible risk of the use of thetreatment parameter.
 10. The article of manufacture of claim 1 whereinthe one or more instructions related to providing a graphicalillustration of a first possible outcome of a use of a treatmentparameter with respect to at least one body portion, based on a firstdataset associated with a first predictive basis comprises: one or moreinstructions related to providing the graphical illustration of thefirst possible outcome based on the first dataset, the first datasetbeing associated with both the first predictive basis and at least oneother predictive basis.
 11. The article of manufacture of claim 1wherein the one or more instructions related to providing a graphicalillustration of a first possible outcome of a use of a treatmentparameter with respect to at least one body portion, based on a firstdataset associated with a first predictive basis comprises: one or moreinstructions related to providing the graphical illustration based onthe first dataset, the first dataset being associated with the firstpredictive basis through a characterization of the first dataset withrespect to one or more of a source of the first dataset a funding of thefirst dataset, a research field of the first dataset, a time period ofcollection of the first dataset, or a location of collection of thefirst dataset.
 12. The article of manufacture of claim 1 wherein the oneor more instructions related to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis comprises: one or more instructions related toproviding the graphical illustration of the first possible outcome ofthe use of the treatment parameter, the treatment parameter includingone or more of at least one target-related tissue ancestry-correlatedbinding site, at least one target-related tissue ancestry-correlatedbinding agent, at least one direct end target at least one discriminatedend target, at least one direct intermediate target, at least onediscriminated intermediate target, at least one treatment agent deliverymechanism relative to the at least one target-related tissueancestry-correlated binding agent, at least one treatment agent, or atleast one treatment agent precursor.
 13. The article of manufacture ofclaim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to receiving a request for the graphicalillustration, the request specifying at least one target-related tissueancestry-correlated binding agent associated with the at least one bodyportion as the treatment parameter; and one or more instructions relatedto providing the graphical illustration of the first possible outcome ofthe use of the at least one target-related tissue ancestry-correlatedbinding agent, the graphical illustration illustrating at least aportion of the at least one body portion.
 14. The article of manufactureof claim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to receiving a request for the graphicalillustration, the request specifying at least one direct end targetassociated with the at least one body portion as the treatmentparameter; and one or more instructions related to providing thegraphical illustration of the first possible outcome of the use of theat least one direct end target, the graphical illustration illustratingat least a portion of the at least one body portion.
 15. The article ofmanufacture of claim 1 wherein the one or more instructions related toproviding a illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to receiving a request for the graphicalillustration, the request specifying at least one discriminated endtarget associated with the at least one body portion as the treatmentparameter; and one or more instructions related to providing thegraphical illustration of the first possible outcome of the use of theat least one discriminated end target, the graphical illustrationillustrating at least a portion of the at least one body portion. 16.The article of manufacture of claim 1 wherein the one or moreinstructions related to providing a graphical illustration of a firstpossible outcome of a use of a treatment parameter with respect to atleast one body portion, based on a first dataset associated with a firstpredictive basis comprises: one or more instructions related toreceiving a request for the graphical illustration, the requestspecifying at least one direct intermediate target associated with theat least one body portion as the treatment parameter; and one or moreinstructions related to providing the graphical illustration of thefirst possible outcome of the use of the at least one directintermediate target, the graphical illustration illustrating at least aportion of the at least one body portion.
 17. The article of manufactureof claim 1 wherein the one or more instructions related to providing agraphical illustration of a first possible outcome of a use of atreatment parameter with respect to at least one body portion, based ona first dataset associated with a first predictive basis comprises: oneor more instructions related to receiving a request for the graphicalillustration, the request specifying at least one discriminatedintermediate target associated with the at least one body portion as thetreatment parameter; and one or more instructions related to providingthe graphical illustration of the first possible outcome of the use ofthe at least one discriminated intermediate target the graphicalillustration illustrating at least a portion of the at least one bodyportion.
 18. The article of manufacture of claim 1 wherein the one ormore instructions related to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion based on a first dataset associated with afirst predictive basis comprises: one or more instructions related toreceiving a request for the graphical illustration the requestspecifying at least one treatment agent associated with the at least onebody portion as the treatment parameter; and one or more instructionsrelated to providing the graphical illustration of the first possibleoutcome of the use of the at least one treatment agent, the graphicalillustration illustrating at least a portion of the at least one bodyportion.
 19. The article of manufacture of claim 1 wherein the one ormore instructions related to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis comprises: one or more instructions related toreceiving a request for the graphical illustration the requestspecifying at least one treatment agent precursor associated with the atleast one body portion as the treatment parameter; and one or moreinstructions related to providing the graphical illustration of thefirst possible outcome of the use of the at least one treatment agentprecursor, the graphical illustration illustrating at least a portion ofthe at least one body portion.
 20. The article of manufacture of claim 1wherein the one or more instructions related to modifying the graphicalillustration to illustrate a second possible outcome of the use of thetreatment parameter, based on a second dataset associated with a secondpredictive basis comprises: one or more instructions related tomodifying a visual indicator associated with the at least one bodyportion within the graphical illustration.
 21. The article ofmanufacture of claim 20, wherein one or more instructions related tomodifying a visual indicator associated with the at least one bodyportion within the graphical illustration comprises: the one or moreinstructions related to modifying the visual indicator associated withthe at least one body portion within the graphical illustration, thevisual indicator includes at least an indication of a generic class ofpatient.
 22. The article of manufacture of claim 1 wherein the one ormore instructions related to modifying the graphical illustration toillustrate a second possible outcome of the use of the treatmentparameter, based on a second dataset associated with a second predictivebasis comprises: one or more instructions related to modifying a visualindicator associated with at least one other body portion within thegraphical illustration.
 23. The article of manufacture of claim 1wherein the one or more instructions related to modifying the graphicalillustration to illustrate a second possible outcome of the use of thetreatment parameter, based on a second dataset associated with a secondpredictive basis comprises: one or more instructions related tomodifying the graphical illustration based on the first dataset and thesecond dataset to incorporate the first predictive basis and the secondpredictive basis in the second possible outcome.
 24. The article ofmanufacture of claim 1 wherein the one or more instructions related tomodifying the graphical illustration to illustrate a second possibleoutcome of the use of the treatment parameter, based on a second datasetassociated with a second predictive basis comprises: one or moreinstructions related to modifying the graphical illustration based onthe second dataset to replace the first predictive basis with the secondpredictive basis in providing the second possible outcome.
 25. Thearticle of manufacture of claim 1 wherein the one or more instructionsrelated to modifying the graphical illustration to illustrate a secondpossible outcome of the use of the treatment parameter, based on asecond dataset associated with a second predictive basis comprises: oneor more instructions related to modifying the graphical illustrationbased on the second dataset to reflect a weighting of the firstpredictive basis with respect to the second predictive basis inproviding the second possible outcome.
 26. The article of manufacture ofclaim 1 wherein the one or more instructions related to modifying thegraphical illustration to illustrate a second possible outcome of theuse of the treatment parameter, based on a second dataset associatedwith a second predictive basis comprises: one or more instructionsrelated to modifying the graphical illustration based on the seconddataset associated with the second predictive basis, the secondpredictive basis being relatively less predictively useful than thefirst predictive basis.
 27. The article of manufacture of claim 1wherein the one or more instructions related to modifying the graphicalillustration to illustrate a second possible outcome of the use of thetreatment parameter, based on a second dataset associated with a secondpredictive basis comprises: one or more instructions related tomodifying the graphical illustration based on the second datasetassociated with the second predictive basis, the second predictive basisbeing relatively more predictively useful than the first predictivebasis.
 28. The article of manufacture of claim 1 wherein the one or moreinstructions related to modifying the graphical illustration toillustrate a second possible outcome of the use of the treatmentparameter, based on a second dataset associated with a second predictivebasis comprises: one or more instructions related to modifying thegraphical illustration to illustrate the second possible outcome withrespect to a potential efficacy of the use of the treatment parameterrelative to the first possible outcome.
 29. The article of manufactureof claim 1 wherein the one or more instructions related to modifying thegraphical illustration to illustrate a second possible outcome of theuse of the treatment parameter, based on a second dataset associatedwith a second predictive basis comprises: one or more instructionsrelated to modifying the graphical illustration to illustrate the secondpossible outcome with respect to a potential risk of the use of thetreatment parameter relative to the first possible outcome.
 30. Thearticle of manufacture of claim 1 wherein the one or more instructionsrelated to modifying the graphical illustration to illustrate a secondpossible outcome of the use of the treatment parameter, based on asecond dataset associated with a second predictive basis comprises: oneor more instructions related to modifying the graphical illustration toillustrate a possible side effect of the use of the treatment parameter.31. The article of manufacture of claim 1 wherein the signal-bearingmedium includes a computer-readable medium.
 32. The article ofmanufacture of claim 1 wherein the signal-bearing medium includes arecordable medium.
 33. The article of manufacture of claim 1 wherein thesignal-bearing medium includes a communications medium.
 34. The articleof manufacture of claim 1, wherein the one or more instructions relatedto providing a graphical illustration of a first possible outcome of ause of a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basisincludes at least one of (i) one or more instructions related toproviding the graphical illustration of the first possible outcomeincluding a possible side effect of the use of the treatment parameter;or (ii) one or more instructions related to providing the graphicalillustration including a representation of the first possible outcomewith respect to a possible efficacy of the use of the treatmentparameter comprises: the one or more instructions related to providingthe graphical illustration of the first possible outcome including thepossible side effect of the use of the treatment parameter, and is notthe one or more instructions related to providing the graphicalillustration including the representation of the first possible outcomewith respect to the possible efficacy of the use of the treatmentparameter.
 35. The article of manufacture of claim 1, wherein the one ormore instructions related to providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis includes at least one of (i) one or moreinstructions related to providing the graphical illustration of thefirst possible outcome including a possible side effect of the use ofthe treatment parameter; or (ii) one or more instructions related toproviding the graphical illustration including a representation of thefirst possible outcome with respect to a possible efficacy of the use ofthe treatment parameter comprises: the one or more instructions relatedto providing the graphical illustration including the representation ofthe first possible outcome with respect to the possible efficacy of theuse of the treatment parameter, and is not the one or more instructionsrelated to providing the graphical illustration of the first possibleoutcome including the possible side effect of the use of the treatmentparameter.
 36. An article of manufacture including at least anon-transitory signal bearing medium bearing at least: (a) one or moreinstructions relating to providing a graphical illustration of a firstpossible outcome of a use of a treatment parameter with respect to atleast one body portion, based on a first dataset associated with a firstpredictive basis, wherein the one or more instructions related toproviding a graphical illustration of a first possible outcome of a useof a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basisincludes at least: (i) one or more instructions related to receiving arequest for the graphical illustration, the request specifying at leastone target-related tissue ancestry-correlated binding site associatedwith the at least one body portion as the treatment parameter; and (ii)one or more instructions related to providing the graphical illustrationof the first possible outcome of the use of the at least onetarget-related tissue ancestry-correlated binding site, the graphicalillustration illustrating at least a portion of the at least one bodyportion; and (b) one or more instructions relating to modifying thegraphical illustration to illustrate a second possible outcome of theuse of the treatment parameter, based on a second dataset associatedwith a second predictive basis.
 37. A system comprising: (a) circuitryconfigured for providing a graphical illustration of a first possibleoutcome of a use of a treatment parameter with respect to at least onebody portion, based on a first dataset associated with a firstpredictive basis, the first dataset being associated with the firstpredictive basis through a characterization of the first dataset, thefirst predictive basis, and/or a relationship between the first datasetand the first predictive basis, wherein the circuitry configured forproviding a graphical illustration of a first possible outcome of a useof a treatment parameter with respect to at least one body portion,based on a first dataset associated with a first predictive basisincludes at least one of: (i) circuitry configured for providing thegraphical illustration of the first possible outcome including apossible side effect of the use of the treatment parameter; or (ii)circuitry configured for providing the graphical illustration includinga representation of the first possible outcome with respect to apossible efficacy of the use of the treatment parameter; and (b)circuitry configured for modifying the graphical illustration toillustrate a second possible outcome of the use of the treatmentparameter, based on a second dataset associated with a second predictivebasis.
 38. A system comprising: (a) means for providing a graphicalillustration of a first possible outcome of a use of a treatmentparameter with respect to at least one body portion, based on a firstdataset associated with a first predictive basis, the first datasetbeing associated with the first predictive basis through acharacterization of the first dataset, the first predictive basis,and/or a relationship between the first dataset and the first predictivebasis, wherein the means for providing a graphical illustration of afirst possible outcome of a use of a treatment parameter with respect toat least one body portion, based on a first dataset associated with afirst predictive basis, includes at least one of: (i) means forproviding the graphical illustration of the first possible outcomeincluding a possible side effect of the use of the treatment parameter;or (ii) means for providing the graphical illustration including arepresentation of the first possible outcome with respect to a possibleefficacy of the use of the treatment parameter; and (b) means formodifying the graphical illustration to illustrate a second possibleoutcome of the use of the treatment parameter, based on a second datasetassociated with a second predictive basis.